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: A->nonzerostate++;
472: noinsert:;
473: }
474: ailen[row] = nrow;
475: }
476: PetscCall(MatSeqAIJRestoreArray(A, &aa));
477: PetscFunctionReturn(PETSC_SUCCESS);
478: }
480: static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
481: {
482: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
483: PetscInt *rp, k, row;
484: PetscInt *ai = a->i;
485: PetscInt *aj = a->j;
486: MatScalar *aa, *ap;
488: PetscFunctionBegin;
489: PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
490: PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz);
492: PetscCall(MatSeqAIJGetArray(A, &aa));
493: for (k = 0; k < m; k++) { /* loop over added rows */
494: row = im[k];
495: rp = aj + ai[row];
496: ap = PetscSafePointerPlusOffset(aa, ai[row]);
498: PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
499: if (!A->structure_only) {
500: if (v) {
501: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
502: v += n;
503: } else {
504: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
505: }
506: }
507: a->ilen[row] = n;
508: a->imax[row] = n;
509: a->i[row + 1] = a->i[row] + n;
510: a->nz += n;
511: }
512: PetscCall(MatSeqAIJRestoreArray(A, &aa));
513: PetscFunctionReturn(PETSC_SUCCESS);
514: }
516: /*@
517: MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.
519: Input Parameters:
520: + A - the `MATSEQAIJ` matrix
521: - nztotal - bound on the number of nonzeros
523: Level: advanced
525: Notes:
526: This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
527: Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used
528: as always with multiple matrix assemblies.
530: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
531: @*/
532: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
533: {
534: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
536: PetscFunctionBegin;
537: PetscCall(PetscLayoutSetUp(A->rmap));
538: PetscCall(PetscLayoutSetUp(A->cmap));
539: a->maxnz = nztotal;
540: if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
541: if (!a->ilen) {
542: PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
543: } else {
544: PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
545: }
547: /* allocate the matrix space */
548: if (A->structure_only) {
549: PetscCall(PetscMalloc1(nztotal, &a->j));
550: PetscCall(PetscMalloc1(A->rmap->n + 1, &a->i));
551: } else {
552: PetscCall(PetscMalloc3(nztotal, &a->a, nztotal, &a->j, A->rmap->n + 1, &a->i));
553: }
554: a->i[0] = 0;
555: if (A->structure_only) {
556: a->singlemalloc = PETSC_FALSE;
557: a->free_a = PETSC_FALSE;
558: } else {
559: a->singlemalloc = PETSC_TRUE;
560: a->free_a = PETSC_TRUE;
561: }
562: a->free_ij = PETSC_TRUE;
563: A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
564: A->preallocated = PETSC_TRUE;
565: PetscFunctionReturn(PETSC_SUCCESS);
566: }
568: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
569: {
570: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
571: PetscInt *rp, k, row;
572: PetscInt *ai = a->i, *ailen = a->ilen;
573: PetscInt *aj = a->j;
574: MatScalar *aa, *ap;
576: PetscFunctionBegin;
577: PetscCall(MatSeqAIJGetArray(A, &aa));
578: for (k = 0; k < m; k++) { /* loop over added rows */
579: row = im[k];
580: 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);
581: rp = aj + ai[row];
582: ap = aa + ai[row];
583: if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
584: if (!A->structure_only) {
585: if (v) {
586: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
587: v += n;
588: } else {
589: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
590: }
591: }
592: ailen[row] = n;
593: a->nz += n;
594: }
595: PetscCall(MatSeqAIJRestoreArray(A, &aa));
596: PetscFunctionReturn(PETSC_SUCCESS);
597: }
599: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
600: {
601: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
602: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
603: PetscInt *ai = a->i, *ailen = a->ilen;
604: const MatScalar *ap, *aa;
606: PetscFunctionBegin;
607: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
608: for (k = 0; k < m; k++) { /* loop over rows */
609: row = im[k];
610: if (row < 0) {
611: v += n;
612: continue;
613: } /* negative row */
614: 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);
615: rp = PetscSafePointerPlusOffset(aj, ai[row]);
616: ap = PetscSafePointerPlusOffset(aa, ai[row]);
617: nrow = ailen[row];
618: for (l = 0; l < n; l++) { /* loop over columns */
619: if (in[l] < 0) {
620: v++;
621: continue;
622: } /* negative column */
623: 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);
624: col = in[l];
625: high = nrow;
626: low = 0; /* assume unsorted */
627: while (high - low > 5) {
628: t = (low + high) / 2;
629: if (rp[t] > col) high = t;
630: else low = t;
631: }
632: for (i = low; i < high; i++) {
633: if (rp[i] > col) break;
634: if (rp[i] == col) {
635: *v++ = ap[i];
636: goto finished;
637: }
638: }
639: *v++ = 0.0;
640: finished:;
641: }
642: }
643: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
644: PetscFunctionReturn(PETSC_SUCCESS);
645: }
647: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
648: {
649: Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data;
650: const PetscScalar *av;
651: PetscInt header[4], M, N, m, nz, i;
652: PetscInt *rowlens;
654: PetscFunctionBegin;
655: PetscCall(PetscViewerSetUp(viewer));
657: M = mat->rmap->N;
658: N = mat->cmap->N;
659: m = mat->rmap->n;
660: nz = A->nz;
662: /* write matrix header */
663: header[0] = MAT_FILE_CLASSID;
664: header[1] = M;
665: header[2] = N;
666: header[3] = nz;
667: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
669: /* fill in and store row lengths */
670: PetscCall(PetscMalloc1(m, &rowlens));
671: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
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: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);
721: /* trigger copy to CPU if needed */
722: PetscCall(MatSeqAIJGetArrayRead(A, &av));
723: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
724: if (format == PETSC_VIEWER_ASCII_MATLAB) {
725: PetscInt nofinalvalue = 0;
726: if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) {
727: /* Need a dummy value to ensure the dimension of the matrix. */
728: nofinalvalue = 1;
729: }
730: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
731: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n));
732: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz));
733: #if defined(PETSC_USE_COMPLEX)
734: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue));
735: #else
736: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue));
737: #endif
738: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n"));
740: for (i = 0; i < m; i++) {
741: for (j = a->i[i]; j < a->i[i + 1]; j++) {
742: #if defined(PETSC_USE_COMPLEX)
743: 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])));
744: #else
745: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j]));
746: #endif
747: }
748: }
749: if (nofinalvalue) {
750: #if defined(PETSC_USE_COMPLEX)
751: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.));
752: #else
753: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0));
754: #endif
755: }
756: PetscCall(PetscObjectGetName((PetscObject)A, &name));
757: PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name));
758: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
759: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
760: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
761: for (i = 0; i < m; i++) {
762: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
763: for (j = a->i[i]; j < a->i[i + 1]; j++) {
764: #if defined(PETSC_USE_COMPLEX)
765: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
766: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
767: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
768: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
769: } else if (PetscRealPart(a->a[j]) != 0.0) {
770: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
771: }
772: #else
773: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
774: #endif
775: }
776: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
777: }
778: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
779: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
780: PetscInt nzd = 0, fshift = 1, *sptr;
781: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
782: PetscCall(PetscMalloc1(m + 1, &sptr));
783: for (i = 0; i < m; i++) {
784: sptr[i] = nzd + 1;
785: for (j = a->i[i]; j < a->i[i + 1]; j++) {
786: if (a->j[j] >= i) {
787: #if defined(PETSC_USE_COMPLEX)
788: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
789: #else
790: if (a->a[j] != 0.0) nzd++;
791: #endif
792: }
793: }
794: }
795: sptr[m] = nzd + 1;
796: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd));
797: for (i = 0; i < m + 1; i += 6) {
798: if (i + 4 < m) {
799: 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]));
800: } else if (i + 3 < m) {
801: 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]));
802: } else if (i + 2 < m) {
803: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3]));
804: } else if (i + 1 < m) {
805: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2]));
806: } else if (i < m) {
807: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1]));
808: } else {
809: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i]));
810: }
811: }
812: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
813: PetscCall(PetscFree(sptr));
814: for (i = 0; i < m; i++) {
815: for (j = a->i[i]; j < a->i[i + 1]; j++) {
816: if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift));
817: }
818: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
819: }
820: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
821: for (i = 0; i < m; i++) {
822: for (j = a->i[i]; j < a->i[i + 1]; j++) {
823: if (a->j[j] >= i) {
824: #if defined(PETSC_USE_COMPLEX)
825: 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])));
826: #else
827: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j]));
828: #endif
829: }
830: }
831: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
832: }
833: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
834: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
835: PetscInt cnt = 0, jcnt;
836: PetscScalar value;
837: #if defined(PETSC_USE_COMPLEX)
838: PetscBool realonly = PETSC_TRUE;
840: for (i = 0; i < a->i[m]; i++) {
841: if (PetscImaginaryPart(a->a[i]) != 0.0) {
842: realonly = PETSC_FALSE;
843: break;
844: }
845: }
846: #endif
848: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
849: for (i = 0; i < m; i++) {
850: jcnt = 0;
851: for (j = 0; j < A->cmap->n; j++) {
852: if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) {
853: value = a->a[cnt++];
854: jcnt++;
855: } else {
856: value = 0.0;
857: }
858: #if defined(PETSC_USE_COMPLEX)
859: if (realonly) {
860: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value)));
861: } else {
862: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value)));
863: }
864: #else
865: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value));
866: #endif
867: }
868: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
869: }
870: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
871: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
872: PetscInt fshift = 1;
873: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
874: #if defined(PETSC_USE_COMPLEX)
875: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n"));
876: #else
877: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n"));
878: #endif
879: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz));
880: for (i = 0; i < m; i++) {
881: for (j = a->i[i]; j < a->i[i + 1]; j++) {
882: #if defined(PETSC_USE_COMPLEX)
883: 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])));
884: #else
885: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j]));
886: #endif
887: }
888: }
889: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
890: } else {
891: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
892: if (A->factortype) {
893: for (i = 0; i < m; i++) {
894: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
895: /* L part */
896: for (j = a->i[i]; j < a->i[i + 1]; j++) {
897: #if defined(PETSC_USE_COMPLEX)
898: if (PetscImaginaryPart(a->a[j]) > 0.0) {
899: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
900: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
901: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
902: } else {
903: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
904: }
905: #else
906: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
907: #endif
908: }
909: /* diagonal */
910: j = a->diag[i];
911: #if defined(PETSC_USE_COMPLEX)
912: if (PetscImaginaryPart(a->a[j]) > 0.0) {
913: 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])));
914: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
915: 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]))));
916: } else {
917: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1.0 / a->a[j])));
918: }
919: #else
920: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1.0 / a->a[j])));
921: #endif
923: /* U part */
924: for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) {
925: #if defined(PETSC_USE_COMPLEX)
926: if (PetscImaginaryPart(a->a[j]) > 0.0) {
927: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
928: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
929: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
930: } else {
931: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
932: }
933: #else
934: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
935: #endif
936: }
937: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
938: }
939: } else {
940: for (i = 0; i < m; i++) {
941: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
942: for (j = a->i[i]; j < a->i[i + 1]; j++) {
943: #if defined(PETSC_USE_COMPLEX)
944: if (PetscImaginaryPart(a->a[j]) > 0.0) {
945: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
946: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
947: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
948: } else {
949: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
950: }
951: #else
952: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
953: #endif
954: }
955: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
956: }
957: }
958: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
959: }
960: PetscCall(PetscViewerFlush(viewer));
961: PetscFunctionReturn(PETSC_SUCCESS);
962: }
964: #include <petscdraw.h>
965: static PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
966: {
967: Mat A = (Mat)Aa;
968: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
969: PetscInt i, j, m = A->rmap->n;
970: int color;
971: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
972: PetscViewer viewer;
973: PetscViewerFormat format;
974: const PetscScalar *aa;
976: PetscFunctionBegin;
977: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
978: PetscCall(PetscViewerGetFormat(viewer, &format));
979: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
981: /* loop over matrix elements drawing boxes */
982: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
983: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
984: PetscDrawCollectiveBegin(draw);
985: /* Blue for negative, Cyan for zero and Red for positive */
986: color = PETSC_DRAW_BLUE;
987: for (i = 0; i < m; i++) {
988: y_l = m - i - 1.0;
989: y_r = y_l + 1.0;
990: for (j = a->i[i]; j < a->i[i + 1]; j++) {
991: x_l = a->j[j];
992: x_r = x_l + 1.0;
993: if (PetscRealPart(aa[j]) >= 0.) continue;
994: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
995: }
996: }
997: color = PETSC_DRAW_CYAN;
998: for (i = 0; i < m; i++) {
999: y_l = m - i - 1.0;
1000: y_r = y_l + 1.0;
1001: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1002: x_l = a->j[j];
1003: x_r = x_l + 1.0;
1004: if (aa[j] != 0.) continue;
1005: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1006: }
1007: }
1008: color = PETSC_DRAW_RED;
1009: for (i = 0; i < m; i++) {
1010: y_l = m - i - 1.0;
1011: y_r = y_l + 1.0;
1012: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1013: x_l = a->j[j];
1014: x_r = x_l + 1.0;
1015: if (PetscRealPart(aa[j]) <= 0.) continue;
1016: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1017: }
1018: }
1019: PetscDrawCollectiveEnd(draw);
1020: } else {
1021: /* use contour shading to indicate magnitude of values */
1022: /* first determine max of all nonzero values */
1023: PetscReal minv = 0.0, maxv = 0.0;
1024: PetscInt nz = a->nz, count = 0;
1025: PetscDraw popup;
1027: for (i = 0; i < nz; i++) {
1028: if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1029: }
1030: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1031: PetscCall(PetscDrawGetPopup(draw, &popup));
1032: PetscCall(PetscDrawScalePopup(popup, minv, maxv));
1034: PetscDrawCollectiveBegin(draw);
1035: for (i = 0; i < m; i++) {
1036: y_l = m - i - 1.0;
1037: y_r = y_l + 1.0;
1038: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1039: x_l = a->j[j];
1040: x_r = x_l + 1.0;
1041: color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1042: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1043: count++;
1044: }
1045: }
1046: PetscDrawCollectiveEnd(draw);
1047: }
1048: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1049: PetscFunctionReturn(PETSC_SUCCESS);
1050: }
1052: #include <petscdraw.h>
1053: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1054: {
1055: PetscDraw draw;
1056: PetscReal xr, yr, xl, yl, h, w;
1057: PetscBool isnull;
1059: PetscFunctionBegin;
1060: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1061: PetscCall(PetscDrawIsNull(draw, &isnull));
1062: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1064: xr = A->cmap->n;
1065: yr = A->rmap->n;
1066: h = yr / 10.0;
1067: w = xr / 10.0;
1068: xr += w;
1069: yr += h;
1070: xl = -w;
1071: yl = -h;
1072: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1073: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1074: PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1075: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1076: PetscCall(PetscDrawSave(draw));
1077: PetscFunctionReturn(PETSC_SUCCESS);
1078: }
1080: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1081: {
1082: PetscBool iascii, isbinary, isdraw;
1084: PetscFunctionBegin;
1085: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1086: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1087: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1088: if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1089: else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1090: else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1091: PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1092: PetscFunctionReturn(PETSC_SUCCESS);
1093: }
1095: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1096: {
1097: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1098: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1099: PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n;
1100: MatScalar *aa = a->a, *ap;
1101: PetscReal ratio = 0.6;
1103: PetscFunctionBegin;
1104: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
1105: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1106: if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1107: /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1108: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1109: PetscFunctionReturn(PETSC_SUCCESS);
1110: }
1112: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1113: for (i = 1; i < m; i++) {
1114: /* move each row back by the amount of empty slots (fshift) before it*/
1115: fshift += imax[i - 1] - ailen[i - 1];
1116: rmax = PetscMax(rmax, ailen[i]);
1117: if (fshift) {
1118: ip = aj + ai[i];
1119: ap = aa + ai[i];
1120: N = ailen[i];
1121: PetscCall(PetscArraymove(ip - fshift, ip, N));
1122: if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1123: }
1124: ai[i] = ai[i - 1] + ailen[i - 1];
1125: }
1126: if (m) {
1127: fshift += imax[m - 1] - ailen[m - 1];
1128: ai[m] = ai[m - 1] + ailen[m - 1];
1129: }
1130: /* reset ilen and imax for each row */
1131: a->nonzerorowcnt = 0;
1132: if (A->structure_only) {
1133: PetscCall(PetscFree(a->imax));
1134: PetscCall(PetscFree(a->ilen));
1135: } else { /* !A->structure_only */
1136: for (i = 0; i < m; i++) {
1137: ailen[i] = imax[i] = ai[i + 1] - ai[i];
1138: a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1139: }
1140: }
1141: a->nz = ai[m];
1142: 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);
1143: PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
1144: a->diagonaldense = PETSC_TRUE;
1145: n = PetscMin(A->rmap->n, A->cmap->n);
1146: for (i = 0; i < n; i++) {
1147: if (a->diag[i] >= ai[i + 1]) {
1148: a->diagonaldense = PETSC_FALSE;
1149: break;
1150: }
1151: }
1152: 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));
1153: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1154: PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));
1156: A->info.mallocs += a->reallocs;
1157: a->reallocs = 0;
1158: A->info.nz_unneeded = (PetscReal)fshift;
1159: a->rmax = rmax;
1161: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1162: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1163: PetscFunctionReturn(PETSC_SUCCESS);
1164: }
1166: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1167: {
1168: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1169: PetscInt i, nz = a->nz;
1170: MatScalar *aa;
1172: PetscFunctionBegin;
1173: PetscCall(MatSeqAIJGetArray(A, &aa));
1174: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1175: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1176: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1177: PetscFunctionReturn(PETSC_SUCCESS);
1178: }
1180: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1181: {
1182: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1183: PetscInt i, nz = a->nz;
1184: MatScalar *aa;
1186: PetscFunctionBegin;
1187: PetscCall(MatSeqAIJGetArray(A, &aa));
1188: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1189: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1190: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1191: PetscFunctionReturn(PETSC_SUCCESS);
1192: }
1194: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1195: {
1196: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1197: MatScalar *aa;
1199: PetscFunctionBegin;
1200: PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1201: PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1202: PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1203: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1204: PetscFunctionReturn(PETSC_SUCCESS);
1205: }
1207: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1208: {
1209: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1211: PetscFunctionBegin;
1212: if (A->hash_active) {
1213: A->ops[0] = a->cops;
1214: PetscCall(PetscHMapIJVDestroy(&a->ht));
1215: PetscCall(PetscFree(a->dnz));
1216: A->hash_active = PETSC_FALSE;
1217: }
1219: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1220: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1221: PetscCall(ISDestroy(&a->row));
1222: PetscCall(ISDestroy(&a->col));
1223: PetscCall(PetscFree(a->diag));
1224: PetscCall(PetscFree(a->ibdiag));
1225: PetscCall(PetscFree(a->imax));
1226: PetscCall(PetscFree(a->ilen));
1227: PetscCall(PetscFree(a->ipre));
1228: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1229: PetscCall(PetscFree(a->solve_work));
1230: PetscCall(ISDestroy(&a->icol));
1231: PetscCall(PetscFree(a->saved_values));
1232: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1233: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1234: PetscCall(PetscFree(A->data));
1236: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1237: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1238: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1239: users reusing the matrix object with different data to incur in obscure segmentation faults
1240: due to different matrix sizes */
1241: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1243: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1244: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1245: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1246: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1247: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1248: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1249: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1250: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1251: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1252: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1253: #if defined(PETSC_HAVE_MKL_SPARSE)
1254: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1255: #endif
1256: #if defined(PETSC_HAVE_CUDA)
1257: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1258: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1259: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1260: #endif
1261: #if defined(PETSC_HAVE_HIP)
1262: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1263: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1265: #endif
1266: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1267: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1268: #endif
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1270: #if defined(PETSC_HAVE_ELEMENTAL)
1271: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1272: #endif
1273: #if defined(PETSC_HAVE_SCALAPACK)
1274: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1275: #endif
1276: #if defined(PETSC_HAVE_HYPRE)
1277: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1278: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1279: #endif
1280: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1282: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1283: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1284: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1285: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1286: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1287: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1288: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1289: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1290: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1291: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1292: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1293: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1294: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1296: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1297: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1302: PetscFunctionReturn(PETSC_SUCCESS);
1303: }
1305: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1306: {
1307: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1309: PetscFunctionBegin;
1310: switch (op) {
1311: case MAT_ROW_ORIENTED:
1312: a->roworiented = flg;
1313: break;
1314: case MAT_KEEP_NONZERO_PATTERN:
1315: a->keepnonzeropattern = flg;
1316: break;
1317: case MAT_NEW_NONZERO_LOCATIONS:
1318: a->nonew = (flg ? 0 : 1);
1319: break;
1320: case MAT_NEW_NONZERO_LOCATION_ERR:
1321: a->nonew = (flg ? -1 : 0);
1322: break;
1323: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1324: a->nonew = (flg ? -2 : 0);
1325: break;
1326: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1327: a->nounused = (flg ? -1 : 0);
1328: break;
1329: case MAT_IGNORE_ZERO_ENTRIES:
1330: a->ignorezeroentries = flg;
1331: break;
1332: case MAT_SPD:
1333: case MAT_SYMMETRIC:
1334: case MAT_STRUCTURALLY_SYMMETRIC:
1335: case MAT_HERMITIAN:
1336: case MAT_SYMMETRY_ETERNAL:
1337: case MAT_STRUCTURE_ONLY:
1338: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1339: case MAT_SPD_ETERNAL:
1340: /* if the diagonal matrix is square it inherits some of the properties above */
1341: break;
1342: case MAT_FORCE_DIAGONAL_ENTRIES:
1343: case MAT_IGNORE_OFF_PROC_ENTRIES:
1344: case MAT_USE_HASH_TABLE:
1345: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1346: break;
1347: case MAT_USE_INODES:
1348: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1349: break;
1350: case MAT_SUBMAT_SINGLEIS:
1351: A->submat_singleis = flg;
1352: break;
1353: case MAT_SORTED_FULL:
1354: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1355: else A->ops->setvalues = MatSetValues_SeqAIJ;
1356: break;
1357: case MAT_FORM_EXPLICIT_TRANSPOSE:
1358: A->form_explicit_transpose = flg;
1359: break;
1360: default:
1361: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1362: }
1363: PetscFunctionReturn(PETSC_SUCCESS);
1364: }
1366: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1367: {
1368: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1369: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1370: PetscScalar *x;
1371: const PetscScalar *aa;
1373: PetscFunctionBegin;
1374: PetscCall(VecGetLocalSize(v, &n));
1375: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1376: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1377: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1378: PetscInt *diag = a->diag;
1379: PetscCall(VecGetArrayWrite(v, &x));
1380: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1381: PetscCall(VecRestoreArrayWrite(v, &x));
1382: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1383: PetscFunctionReturn(PETSC_SUCCESS);
1384: }
1386: PetscCall(VecGetArrayWrite(v, &x));
1387: for (i = 0; i < n; i++) {
1388: x[i] = 0.0;
1389: for (j = ai[i]; j < ai[i + 1]; j++) {
1390: if (aj[j] == i) {
1391: x[i] = aa[j];
1392: break;
1393: }
1394: }
1395: }
1396: PetscCall(VecRestoreArrayWrite(v, &x));
1397: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1398: PetscFunctionReturn(PETSC_SUCCESS);
1399: }
1401: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1402: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1403: {
1404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1405: const MatScalar *aa;
1406: PetscScalar *y;
1407: const PetscScalar *x;
1408: PetscInt m = A->rmap->n;
1409: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1410: const MatScalar *v;
1411: PetscScalar alpha;
1412: PetscInt n, i, j;
1413: const PetscInt *idx, *ii, *ridx = NULL;
1414: Mat_CompressedRow cprow = a->compressedrow;
1415: PetscBool usecprow = cprow.use;
1416: #endif
1418: PetscFunctionBegin;
1419: if (zz != yy) PetscCall(VecCopy(zz, yy));
1420: PetscCall(VecGetArrayRead(xx, &x));
1421: PetscCall(VecGetArray(yy, &y));
1422: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1424: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1425: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1426: #else
1427: if (usecprow) {
1428: m = cprow.nrows;
1429: ii = cprow.i;
1430: ridx = cprow.rindex;
1431: } else {
1432: ii = a->i;
1433: }
1434: for (i = 0; i < m; i++) {
1435: idx = a->j + ii[i];
1436: v = aa + ii[i];
1437: n = ii[i + 1] - ii[i];
1438: if (usecprow) {
1439: alpha = x[ridx[i]];
1440: } else {
1441: alpha = x[i];
1442: }
1443: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1444: }
1445: #endif
1446: PetscCall(PetscLogFlops(2.0 * a->nz));
1447: PetscCall(VecRestoreArrayRead(xx, &x));
1448: PetscCall(VecRestoreArray(yy, &y));
1449: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1450: PetscFunctionReturn(PETSC_SUCCESS);
1451: }
1453: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1454: {
1455: PetscFunctionBegin;
1456: PetscCall(VecSet(yy, 0.0));
1457: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1458: PetscFunctionReturn(PETSC_SUCCESS);
1459: }
1461: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1463: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1464: {
1465: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1466: PetscScalar *y;
1467: const PetscScalar *x;
1468: const MatScalar *aa, *a_a;
1469: PetscInt m = A->rmap->n;
1470: const PetscInt *aj, *ii, *ridx = NULL;
1471: PetscInt n, i;
1472: PetscScalar sum;
1473: PetscBool usecprow = a->compressedrow.use;
1475: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1476: #pragma disjoint(*x, *y, *aa)
1477: #endif
1479: PetscFunctionBegin;
1480: if (a->inode.use && a->inode.checked) {
1481: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1482: PetscFunctionReturn(PETSC_SUCCESS);
1483: }
1484: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1485: PetscCall(VecGetArrayRead(xx, &x));
1486: PetscCall(VecGetArray(yy, &y));
1487: ii = a->i;
1488: if (usecprow) { /* use compressed row format */
1489: PetscCall(PetscArrayzero(y, m));
1490: m = a->compressedrow.nrows;
1491: ii = a->compressedrow.i;
1492: ridx = a->compressedrow.rindex;
1493: for (i = 0; i < m; i++) {
1494: n = ii[i + 1] - ii[i];
1495: aj = a->j + ii[i];
1496: aa = a_a + ii[i];
1497: sum = 0.0;
1498: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1499: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1500: y[*ridx++] = sum;
1501: }
1502: } else { /* do not use compressed row format */
1503: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1504: aj = a->j;
1505: aa = a_a;
1506: fortranmultaij_(&m, x, ii, aj, aa, y);
1507: #else
1508: for (i = 0; i < m; i++) {
1509: n = ii[i + 1] - ii[i];
1510: aj = a->j + ii[i];
1511: aa = a_a + ii[i];
1512: sum = 0.0;
1513: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1514: y[i] = sum;
1515: }
1516: #endif
1517: }
1518: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1519: PetscCall(VecRestoreArrayRead(xx, &x));
1520: PetscCall(VecRestoreArray(yy, &y));
1521: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1522: PetscFunctionReturn(PETSC_SUCCESS);
1523: }
1525: // HACK!!!!! Used by src/mat/tests/ex170.c
1526: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1527: {
1528: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1529: PetscScalar *y;
1530: const PetscScalar *x;
1531: const MatScalar *aa, *a_a;
1532: PetscInt m = A->rmap->n;
1533: const PetscInt *aj, *ii, *ridx = NULL;
1534: PetscInt n, i, nonzerorow = 0;
1535: PetscScalar sum;
1536: PetscBool usecprow = a->compressedrow.use;
1538: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1539: #pragma disjoint(*x, *y, *aa)
1540: #endif
1542: PetscFunctionBegin;
1543: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1544: PetscCall(VecGetArrayRead(xx, &x));
1545: PetscCall(VecGetArray(yy, &y));
1546: if (usecprow) { /* use compressed row format */
1547: m = a->compressedrow.nrows;
1548: ii = a->compressedrow.i;
1549: ridx = a->compressedrow.rindex;
1550: for (i = 0; i < m; i++) {
1551: n = ii[i + 1] - ii[i];
1552: aj = a->j + ii[i];
1553: aa = a_a + ii[i];
1554: sum = 0.0;
1555: nonzerorow += (n > 0);
1556: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1557: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1558: y[*ridx++] = sum;
1559: }
1560: } else { /* do not use compressed row format */
1561: ii = a->i;
1562: for (i = 0; i < m; i++) {
1563: n = ii[i + 1] - ii[i];
1564: aj = a->j + ii[i];
1565: aa = a_a + ii[i];
1566: sum = 0.0;
1567: nonzerorow += (n > 0);
1568: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1569: y[i] = sum;
1570: }
1571: }
1572: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1573: PetscCall(VecRestoreArrayRead(xx, &x));
1574: PetscCall(VecRestoreArray(yy, &y));
1575: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1576: PetscFunctionReturn(PETSC_SUCCESS);
1577: }
1579: // HACK!!!!! Used by src/mat/tests/ex170.c
1580: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1581: {
1582: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1583: PetscScalar *y, *z;
1584: const PetscScalar *x;
1585: const MatScalar *aa, *a_a;
1586: PetscInt m = A->rmap->n, *aj, *ii;
1587: PetscInt n, i, *ridx = NULL;
1588: PetscScalar sum;
1589: PetscBool usecprow = a->compressedrow.use;
1591: PetscFunctionBegin;
1592: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1593: PetscCall(VecGetArrayRead(xx, &x));
1594: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1595: if (usecprow) { /* use compressed row format */
1596: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1597: m = a->compressedrow.nrows;
1598: ii = a->compressedrow.i;
1599: ridx = a->compressedrow.rindex;
1600: for (i = 0; i < m; i++) {
1601: n = ii[i + 1] - ii[i];
1602: aj = a->j + ii[i];
1603: aa = a_a + ii[i];
1604: sum = y[*ridx];
1605: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1606: z[*ridx++] = sum;
1607: }
1608: } else { /* do not use compressed row format */
1609: ii = a->i;
1610: for (i = 0; i < m; i++) {
1611: n = ii[i + 1] - ii[i];
1612: aj = a->j + ii[i];
1613: aa = a_a + ii[i];
1614: sum = y[i];
1615: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1616: z[i] = sum;
1617: }
1618: }
1619: PetscCall(PetscLogFlops(2.0 * a->nz));
1620: PetscCall(VecRestoreArrayRead(xx, &x));
1621: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1622: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1623: PetscFunctionReturn(PETSC_SUCCESS);
1624: }
1626: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1627: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1628: {
1629: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1630: PetscScalar *y, *z;
1631: const PetscScalar *x;
1632: const MatScalar *aa, *a_a;
1633: const PetscInt *aj, *ii, *ridx = NULL;
1634: PetscInt m = A->rmap->n, n, i;
1635: PetscScalar sum;
1636: PetscBool usecprow = a->compressedrow.use;
1638: PetscFunctionBegin;
1639: if (a->inode.use && a->inode.checked) {
1640: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1641: PetscFunctionReturn(PETSC_SUCCESS);
1642: }
1643: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1644: PetscCall(VecGetArrayRead(xx, &x));
1645: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1646: if (usecprow) { /* use compressed row format */
1647: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1648: m = a->compressedrow.nrows;
1649: ii = a->compressedrow.i;
1650: ridx = a->compressedrow.rindex;
1651: for (i = 0; i < m; i++) {
1652: n = ii[i + 1] - ii[i];
1653: aj = a->j + ii[i];
1654: aa = a_a + ii[i];
1655: sum = y[*ridx];
1656: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1657: z[*ridx++] = sum;
1658: }
1659: } else { /* do not use compressed row format */
1660: ii = a->i;
1661: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1662: aj = a->j;
1663: aa = a_a;
1664: fortranmultaddaij_(&m, x, ii, aj, aa, y, z);
1665: #else
1666: for (i = 0; i < m; i++) {
1667: n = ii[i + 1] - ii[i];
1668: aj = a->j + ii[i];
1669: aa = a_a + ii[i];
1670: sum = y[i];
1671: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1672: z[i] = sum;
1673: }
1674: #endif
1675: }
1676: PetscCall(PetscLogFlops(2.0 * a->nz));
1677: PetscCall(VecRestoreArrayRead(xx, &x));
1678: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1679: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1680: PetscFunctionReturn(PETSC_SUCCESS);
1681: }
1683: /*
1684: Adds diagonal pointers to sparse matrix structure.
1685: */
1686: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1687: {
1688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1689: PetscInt i, j, m = A->rmap->n;
1690: PetscBool alreadySet = PETSC_TRUE;
1692: PetscFunctionBegin;
1693: if (!a->diag) {
1694: PetscCall(PetscMalloc1(m, &a->diag));
1695: alreadySet = PETSC_FALSE;
1696: }
1697: for (i = 0; i < A->rmap->n; i++) {
1698: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1699: if (alreadySet) {
1700: PetscInt pos = a->diag[i];
1701: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1702: }
1704: a->diag[i] = a->i[i + 1];
1705: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1706: if (a->j[j] == i) {
1707: a->diag[i] = j;
1708: break;
1709: }
1710: }
1711: }
1712: PetscFunctionReturn(PETSC_SUCCESS);
1713: }
1715: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1716: {
1717: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1718: const PetscInt *diag = (const PetscInt *)a->diag;
1719: const PetscInt *ii = (const PetscInt *)a->i;
1720: PetscInt i, *mdiag = NULL;
1721: PetscInt cnt = 0; /* how many diagonals are missing */
1723: PetscFunctionBegin;
1724: if (!A->preallocated || !a->nz) {
1725: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1726: PetscCall(MatShift_Basic(A, v));
1727: PetscFunctionReturn(PETSC_SUCCESS);
1728: }
1730: if (a->diagonaldense) {
1731: cnt = 0;
1732: } else {
1733: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1734: for (i = 0; i < A->rmap->n; i++) {
1735: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1736: cnt++;
1737: mdiag[i] = 1;
1738: }
1739: }
1740: }
1741: if (!cnt) {
1742: PetscCall(MatShift_Basic(A, v));
1743: } else {
1744: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1745: PetscInt *oldj = a->j, *oldi = a->i;
1746: PetscBool singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij;
1747: const PetscScalar *Aa;
1749: PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1750: PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));
1752: a->a = NULL;
1753: a->j = NULL;
1754: a->i = NULL;
1755: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1756: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1757: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1759: /* copy old values into new matrix data structure */
1760: for (i = 0; i < A->rmap->n; i++) {
1761: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1762: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1763: }
1764: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1765: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1766: if (singlemalloc) {
1767: PetscCall(PetscFree3(olda, oldj, oldi));
1768: } else {
1769: if (free_a) PetscCall(PetscFree(olda));
1770: if (free_ij) PetscCall(PetscFree(oldj));
1771: if (free_ij) PetscCall(PetscFree(oldi));
1772: }
1773: }
1774: PetscCall(PetscFree(mdiag));
1775: a->diagonaldense = PETSC_TRUE;
1776: PetscFunctionReturn(PETSC_SUCCESS);
1777: }
1779: /*
1780: Checks for missing diagonals
1781: */
1782: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1783: {
1784: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1785: PetscInt *diag, *ii = a->i, i;
1787: PetscFunctionBegin;
1788: *missing = PETSC_FALSE;
1789: if (A->rmap->n > 0 && !ii) {
1790: *missing = PETSC_TRUE;
1791: if (d) *d = 0;
1792: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1793: } else {
1794: PetscInt n;
1795: n = PetscMin(A->rmap->n, A->cmap->n);
1796: diag = a->diag;
1797: for (i = 0; i < n; i++) {
1798: if (diag[i] >= ii[i + 1]) {
1799: *missing = PETSC_TRUE;
1800: if (d) *d = i;
1801: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1802: break;
1803: }
1804: }
1805: }
1806: PetscFunctionReturn(PETSC_SUCCESS);
1807: }
1809: #include <petscblaslapack.h>
1810: #include <petsc/private/kernels/blockinvert.h>
1812: /*
1813: Note that values is allocated externally by the PC and then passed into this routine
1814: */
1815: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1816: {
1817: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1818: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1819: const PetscReal shift = 0.0;
1820: PetscInt ipvt[5];
1821: PetscCount flops = 0;
1822: PetscScalar work[25], *v_work;
1824: PetscFunctionBegin;
1825: allowzeropivot = PetscNot(A->erroriffailure);
1826: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1827: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1828: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1829: PetscCall(PetscMalloc1(bsizemax, &indx));
1830: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1831: ncnt = 0;
1832: for (i = 0; i < nblocks; i++) {
1833: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1834: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1835: switch (bsizes[i]) {
1836: case 1:
1837: *diag = 1.0 / (*diag);
1838: break;
1839: case 2:
1840: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1841: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1842: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1843: break;
1844: case 3:
1845: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1846: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1847: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1848: break;
1849: case 4:
1850: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1851: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1852: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1853: break;
1854: case 5:
1855: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1856: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1857: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1858: break;
1859: case 6:
1860: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1861: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1862: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1863: break;
1864: case 7:
1865: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1866: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1867: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1868: break;
1869: default:
1870: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1871: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1872: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1873: }
1874: ncnt += bsizes[i];
1875: diag += bsizes[i] * bsizes[i];
1876: flops += 2 * PetscPowInt(bsizes[i], 3) / 3;
1877: }
1878: PetscCall(PetscLogFlops(flops));
1879: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1880: PetscCall(PetscFree(indx));
1881: PetscFunctionReturn(PETSC_SUCCESS);
1882: }
1884: /*
1885: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1886: */
1887: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1888: {
1889: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1890: PetscInt i, *diag, m = A->rmap->n;
1891: const MatScalar *v;
1892: PetscScalar *idiag, *mdiag;
1894: PetscFunctionBegin;
1895: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1896: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1897: diag = a->diag;
1898: if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }
1900: mdiag = a->mdiag;
1901: idiag = a->idiag;
1902: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1903: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1904: for (i = 0; i < m; i++) {
1905: mdiag[i] = v[diag[i]];
1906: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1907: if (PetscRealPart(fshift)) {
1908: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1909: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1910: A->factorerror_zeropivot_value = 0.0;
1911: A->factorerror_zeropivot_row = i;
1912: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1913: }
1914: idiag[i] = 1.0 / v[diag[i]];
1915: }
1916: PetscCall(PetscLogFlops(m));
1917: } else {
1918: for (i = 0; i < m; i++) {
1919: mdiag[i] = v[diag[i]];
1920: idiag[i] = omega / (fshift + v[diag[i]]);
1921: }
1922: PetscCall(PetscLogFlops(2.0 * m));
1923: }
1924: a->idiagvalid = PETSC_TRUE;
1925: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1926: PetscFunctionReturn(PETSC_SUCCESS);
1927: }
1929: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1930: {
1931: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1932: PetscScalar *x, d, sum, *t, scale;
1933: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1934: const PetscScalar *b, *bs, *xb, *ts;
1935: PetscInt n, m = A->rmap->n, i;
1936: const PetscInt *idx, *diag;
1938: PetscFunctionBegin;
1939: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1940: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1941: PetscFunctionReturn(PETSC_SUCCESS);
1942: }
1943: its = its * lits;
1945: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1946: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1947: a->fshift = fshift;
1948: a->omega = omega;
1950: diag = a->diag;
1951: t = a->ssor_work;
1952: idiag = a->idiag;
1953: mdiag = a->mdiag;
1955: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1956: PetscCall(VecGetArray(xx, &x));
1957: PetscCall(VecGetArrayRead(bb, &b));
1958: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1959: if (flag == SOR_APPLY_UPPER) {
1960: /* apply (U + D/omega) to the vector */
1961: bs = b;
1962: for (i = 0; i < m; i++) {
1963: d = fshift + mdiag[i];
1964: n = a->i[i + 1] - diag[i] - 1;
1965: idx = a->j + diag[i] + 1;
1966: v = aa + diag[i] + 1;
1967: sum = b[i] * d / omega;
1968: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1969: x[i] = sum;
1970: }
1971: PetscCall(VecRestoreArray(xx, &x));
1972: PetscCall(VecRestoreArrayRead(bb, &b));
1973: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1974: PetscCall(PetscLogFlops(a->nz));
1975: PetscFunctionReturn(PETSC_SUCCESS);
1976: }
1978: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1979: if (flag & SOR_EISENSTAT) {
1980: /* Let A = L + U + D; where L is lower triangular,
1981: U is upper triangular, E = D/omega; This routine applies
1983: (L + E)^{-1} A (U + E)^{-1}
1985: to a vector efficiently using Eisenstat's trick.
1986: */
1987: scale = (2.0 / omega) - 1.0;
1989: /* x = (E + U)^{-1} b */
1990: for (i = m - 1; i >= 0; i--) {
1991: n = a->i[i + 1] - diag[i] - 1;
1992: idx = a->j + diag[i] + 1;
1993: v = aa + diag[i] + 1;
1994: sum = b[i];
1995: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1996: x[i] = sum * idiag[i];
1997: }
1999: /* t = b - (2*E - D)x */
2000: v = aa;
2001: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
2003: /* t = (E + L)^{-1}t */
2004: ts = t;
2005: diag = a->diag;
2006: for (i = 0; i < m; i++) {
2007: n = diag[i] - a->i[i];
2008: idx = a->j + a->i[i];
2009: v = aa + a->i[i];
2010: sum = t[i];
2011: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2012: t[i] = sum * idiag[i];
2013: /* x = x + t */
2014: x[i] += t[i];
2015: }
2017: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2018: PetscCall(VecRestoreArray(xx, &x));
2019: PetscCall(VecRestoreArrayRead(bb, &b));
2020: PetscFunctionReturn(PETSC_SUCCESS);
2021: }
2022: if (flag & SOR_ZERO_INITIAL_GUESS) {
2023: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2024: for (i = 0; i < m; i++) {
2025: n = diag[i] - a->i[i];
2026: idx = a->j + a->i[i];
2027: v = aa + a->i[i];
2028: sum = b[i];
2029: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2030: t[i] = sum;
2031: x[i] = sum * idiag[i];
2032: }
2033: xb = t;
2034: PetscCall(PetscLogFlops(a->nz));
2035: } else xb = b;
2036: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2037: for (i = m - 1; i >= 0; i--) {
2038: n = a->i[i + 1] - diag[i] - 1;
2039: idx = a->j + diag[i] + 1;
2040: v = aa + diag[i] + 1;
2041: sum = xb[i];
2042: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2043: if (xb == b) {
2044: x[i] = sum * idiag[i];
2045: } else {
2046: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2047: }
2048: }
2049: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2050: }
2051: its--;
2052: }
2053: while (its--) {
2054: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2055: for (i = 0; i < m; i++) {
2056: /* lower */
2057: n = diag[i] - a->i[i];
2058: idx = a->j + a->i[i];
2059: v = aa + a->i[i];
2060: sum = b[i];
2061: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2062: t[i] = sum; /* save application of the lower-triangular part */
2063: /* upper */
2064: n = a->i[i + 1] - diag[i] - 1;
2065: idx = a->j + diag[i] + 1;
2066: v = aa + diag[i] + 1;
2067: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2068: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2069: }
2070: xb = t;
2071: PetscCall(PetscLogFlops(2.0 * a->nz));
2072: } else xb = b;
2073: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2074: for (i = m - 1; i >= 0; i--) {
2075: sum = xb[i];
2076: if (xb == b) {
2077: /* whole matrix (no checkpointing available) */
2078: n = a->i[i + 1] - a->i[i];
2079: idx = a->j + a->i[i];
2080: v = aa + a->i[i];
2081: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2082: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2083: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2084: n = a->i[i + 1] - diag[i] - 1;
2085: idx = a->j + diag[i] + 1;
2086: v = aa + diag[i] + 1;
2087: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2088: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2089: }
2090: }
2091: if (xb == b) {
2092: PetscCall(PetscLogFlops(2.0 * a->nz));
2093: } else {
2094: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2095: }
2096: }
2097: }
2098: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2099: PetscCall(VecRestoreArray(xx, &x));
2100: PetscCall(VecRestoreArrayRead(bb, &b));
2101: PetscFunctionReturn(PETSC_SUCCESS);
2102: }
2104: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2105: {
2106: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2108: PetscFunctionBegin;
2109: info->block_size = 1.0;
2110: info->nz_allocated = a->maxnz;
2111: info->nz_used = a->nz;
2112: info->nz_unneeded = (a->maxnz - a->nz);
2113: info->assemblies = A->num_ass;
2114: info->mallocs = A->info.mallocs;
2115: info->memory = 0; /* REVIEW ME */
2116: if (A->factortype) {
2117: info->fill_ratio_given = A->info.fill_ratio_given;
2118: info->fill_ratio_needed = A->info.fill_ratio_needed;
2119: info->factor_mallocs = A->info.factor_mallocs;
2120: } else {
2121: info->fill_ratio_given = 0;
2122: info->fill_ratio_needed = 0;
2123: info->factor_mallocs = 0;
2124: }
2125: PetscFunctionReturn(PETSC_SUCCESS);
2126: }
2128: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2129: {
2130: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2131: PetscInt i, m = A->rmap->n - 1;
2132: const PetscScalar *xx;
2133: PetscScalar *bb, *aa;
2134: PetscInt d = 0;
2136: PetscFunctionBegin;
2137: if (x && b) {
2138: PetscCall(VecGetArrayRead(x, &xx));
2139: PetscCall(VecGetArray(b, &bb));
2140: for (i = 0; i < N; i++) {
2141: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2142: if (rows[i] >= A->cmap->n) continue;
2143: bb[rows[i]] = diag * xx[rows[i]];
2144: }
2145: PetscCall(VecRestoreArrayRead(x, &xx));
2146: PetscCall(VecRestoreArray(b, &bb));
2147: }
2149: PetscCall(MatSeqAIJGetArray(A, &aa));
2150: if (a->keepnonzeropattern) {
2151: for (i = 0; i < N; i++) {
2152: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2153: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2154: }
2155: if (diag != 0.0) {
2156: for (i = 0; i < N; i++) {
2157: d = rows[i];
2158: if (rows[i] >= A->cmap->n) continue;
2159: 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);
2160: }
2161: for (i = 0; i < N; i++) {
2162: if (rows[i] >= A->cmap->n) continue;
2163: aa[a->diag[rows[i]]] = diag;
2164: }
2165: }
2166: } else {
2167: if (diag != 0.0) {
2168: for (i = 0; i < N; i++) {
2169: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2170: if (a->ilen[rows[i]] > 0) {
2171: if (rows[i] >= A->cmap->n) {
2172: a->ilen[rows[i]] = 0;
2173: } else {
2174: a->ilen[rows[i]] = 1;
2175: aa[a->i[rows[i]]] = diag;
2176: a->j[a->i[rows[i]]] = rows[i];
2177: }
2178: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2179: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2180: }
2181: }
2182: } else {
2183: for (i = 0; i < N; i++) {
2184: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2185: a->ilen[rows[i]] = 0;
2186: }
2187: }
2188: A->nonzerostate++;
2189: }
2190: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2191: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2192: PetscFunctionReturn(PETSC_SUCCESS);
2193: }
2195: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2196: {
2197: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2198: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2199: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2200: const PetscScalar *xx;
2201: PetscScalar *bb, *aa;
2203: PetscFunctionBegin;
2204: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2205: PetscCall(MatSeqAIJGetArray(A, &aa));
2206: if (x && b) {
2207: PetscCall(VecGetArrayRead(x, &xx));
2208: PetscCall(VecGetArray(b, &bb));
2209: vecs = PETSC_TRUE;
2210: }
2211: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2212: for (i = 0; i < N; i++) {
2213: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2214: PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));
2216: zeroed[rows[i]] = PETSC_TRUE;
2217: }
2218: for (i = 0; i < A->rmap->n; i++) {
2219: if (!zeroed[i]) {
2220: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2221: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2222: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2223: aa[j] = 0.0;
2224: }
2225: }
2226: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2227: }
2228: if (x && b) {
2229: PetscCall(VecRestoreArrayRead(x, &xx));
2230: PetscCall(VecRestoreArray(b, &bb));
2231: }
2232: PetscCall(PetscFree(zeroed));
2233: if (diag != 0.0) {
2234: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2235: if (missing) {
2236: for (i = 0; i < N; i++) {
2237: if (rows[i] >= A->cmap->N) continue;
2238: 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]);
2239: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2240: }
2241: } else {
2242: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2243: }
2244: }
2245: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2246: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2247: PetscFunctionReturn(PETSC_SUCCESS);
2248: }
2250: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2251: {
2252: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2253: const PetscScalar *aa;
2255: PetscFunctionBegin;
2256: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2257: *nz = a->i[row + 1] - a->i[row];
2258: if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2259: if (idx) {
2260: if (*nz && a->j) *idx = a->j + a->i[row];
2261: else *idx = NULL;
2262: }
2263: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2264: PetscFunctionReturn(PETSC_SUCCESS);
2265: }
2267: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2268: {
2269: PetscFunctionBegin;
2270: PetscFunctionReturn(PETSC_SUCCESS);
2271: }
2273: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2274: {
2275: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2276: const MatScalar *v;
2277: PetscReal sum = 0.0;
2278: PetscInt i, j;
2280: PetscFunctionBegin;
2281: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2282: if (type == NORM_FROBENIUS) {
2283: #if defined(PETSC_USE_REAL___FP16)
2284: PetscBLASInt one = 1, nz = a->nz;
2285: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2286: #else
2287: for (i = 0; i < a->nz; i++) {
2288: sum += PetscRealPart(PetscConj(*v) * (*v));
2289: v++;
2290: }
2291: *nrm = PetscSqrtReal(sum);
2292: #endif
2293: PetscCall(PetscLogFlops(2.0 * a->nz));
2294: } else if (type == NORM_1) {
2295: PetscReal *tmp;
2296: PetscInt *jj = a->j;
2297: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2298: *nrm = 0.0;
2299: for (j = 0; j < a->nz; j++) {
2300: tmp[*jj++] += PetscAbsScalar(*v);
2301: v++;
2302: }
2303: for (j = 0; j < A->cmap->n; j++) {
2304: if (tmp[j] > *nrm) *nrm = tmp[j];
2305: }
2306: PetscCall(PetscFree(tmp));
2307: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2308: } else if (type == NORM_INFINITY) {
2309: *nrm = 0.0;
2310: for (j = 0; j < A->rmap->n; j++) {
2311: const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2312: sum = 0.0;
2313: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2314: sum += PetscAbsScalar(*v2);
2315: v2++;
2316: }
2317: if (sum > *nrm) *nrm = sum;
2318: }
2319: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2320: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2321: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2322: PetscFunctionReturn(PETSC_SUCCESS);
2323: }
2325: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2326: {
2327: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2328: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2329: const MatScalar *va, *vb;
2330: PetscInt ma, na, mb, nb, i;
2332: PetscFunctionBegin;
2333: PetscCall(MatGetSize(A, &ma, &na));
2334: PetscCall(MatGetSize(B, &mb, &nb));
2335: if (ma != nb || na != mb) {
2336: *f = PETSC_FALSE;
2337: PetscFunctionReturn(PETSC_SUCCESS);
2338: }
2339: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2340: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2341: aii = aij->i;
2342: bii = bij->i;
2343: adx = aij->j;
2344: bdx = bij->j;
2345: PetscCall(PetscMalloc1(ma, &aptr));
2346: PetscCall(PetscMalloc1(mb, &bptr));
2347: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2348: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2350: *f = PETSC_TRUE;
2351: for (i = 0; i < ma; i++) {
2352: while (aptr[i] < aii[i + 1]) {
2353: PetscInt idc, idr;
2354: PetscScalar vc, vr;
2355: /* column/row index/value */
2356: idc = adx[aptr[i]];
2357: idr = bdx[bptr[idc]];
2358: vc = va[aptr[i]];
2359: vr = vb[bptr[idc]];
2360: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2361: *f = PETSC_FALSE;
2362: goto done;
2363: } else {
2364: aptr[i]++;
2365: if (B || i != idc) bptr[idc]++;
2366: }
2367: }
2368: }
2369: done:
2370: PetscCall(PetscFree(aptr));
2371: PetscCall(PetscFree(bptr));
2372: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2373: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2374: PetscFunctionReturn(PETSC_SUCCESS);
2375: }
2377: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2378: {
2379: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2380: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2381: MatScalar *va, *vb;
2382: PetscInt ma, na, mb, nb, i;
2384: PetscFunctionBegin;
2385: PetscCall(MatGetSize(A, &ma, &na));
2386: PetscCall(MatGetSize(B, &mb, &nb));
2387: if (ma != nb || na != mb) {
2388: *f = PETSC_FALSE;
2389: PetscFunctionReturn(PETSC_SUCCESS);
2390: }
2391: aii = aij->i;
2392: bii = bij->i;
2393: adx = aij->j;
2394: bdx = bij->j;
2395: va = aij->a;
2396: vb = bij->a;
2397: PetscCall(PetscMalloc1(ma, &aptr));
2398: PetscCall(PetscMalloc1(mb, &bptr));
2399: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2400: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2402: *f = PETSC_TRUE;
2403: for (i = 0; i < ma; i++) {
2404: while (aptr[i] < aii[i + 1]) {
2405: PetscInt idc, idr;
2406: PetscScalar vc, vr;
2407: /* column/row index/value */
2408: idc = adx[aptr[i]];
2409: idr = bdx[bptr[idc]];
2410: vc = va[aptr[i]];
2411: vr = vb[bptr[idc]];
2412: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2413: *f = PETSC_FALSE;
2414: goto done;
2415: } else {
2416: aptr[i]++;
2417: if (B || i != idc) bptr[idc]++;
2418: }
2419: }
2420: }
2421: done:
2422: PetscCall(PetscFree(aptr));
2423: PetscCall(PetscFree(bptr));
2424: PetscFunctionReturn(PETSC_SUCCESS);
2425: }
2427: static PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2428: {
2429: PetscFunctionBegin;
2430: PetscCall(MatIsTranspose_SeqAIJ(A, A, tol, f));
2431: PetscFunctionReturn(PETSC_SUCCESS);
2432: }
2434: static PetscErrorCode MatIsHermitian_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2435: {
2436: PetscFunctionBegin;
2437: PetscCall(MatIsHermitianTranspose_SeqAIJ(A, A, tol, f));
2438: PetscFunctionReturn(PETSC_SUCCESS);
2439: }
2441: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2442: {
2443: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2444: const PetscScalar *l, *r;
2445: PetscScalar x;
2446: MatScalar *v;
2447: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2448: const PetscInt *jj;
2450: PetscFunctionBegin;
2451: if (ll) {
2452: /* The local size is used so that VecMPI can be passed to this routine
2453: by MatDiagonalScale_MPIAIJ */
2454: PetscCall(VecGetLocalSize(ll, &m));
2455: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2456: PetscCall(VecGetArrayRead(ll, &l));
2457: PetscCall(MatSeqAIJGetArray(A, &v));
2458: for (i = 0; i < m; i++) {
2459: x = l[i];
2460: M = a->i[i + 1] - a->i[i];
2461: for (j = 0; j < M; j++) (*v++) *= x;
2462: }
2463: PetscCall(VecRestoreArrayRead(ll, &l));
2464: PetscCall(PetscLogFlops(nz));
2465: PetscCall(MatSeqAIJRestoreArray(A, &v));
2466: }
2467: if (rr) {
2468: PetscCall(VecGetLocalSize(rr, &n));
2469: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2470: PetscCall(VecGetArrayRead(rr, &r));
2471: PetscCall(MatSeqAIJGetArray(A, &v));
2472: jj = a->j;
2473: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2474: PetscCall(MatSeqAIJRestoreArray(A, &v));
2475: PetscCall(VecRestoreArrayRead(rr, &r));
2476: PetscCall(PetscLogFlops(nz));
2477: }
2478: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2479: PetscFunctionReturn(PETSC_SUCCESS);
2480: }
2482: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2483: {
2484: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2485: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2486: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2487: const PetscInt *irow, *icol;
2488: const PetscScalar *aa;
2489: PetscInt nrows, ncols;
2490: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2491: MatScalar *a_new, *mat_a, *c_a;
2492: Mat C;
2493: PetscBool stride;
2495: PetscFunctionBegin;
2496: PetscCall(ISGetIndices(isrow, &irow));
2497: PetscCall(ISGetLocalSize(isrow, &nrows));
2498: PetscCall(ISGetLocalSize(iscol, &ncols));
2500: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2501: if (stride) {
2502: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2503: } else {
2504: first = 0;
2505: step = 0;
2506: }
2507: if (stride && step == 1) {
2508: /* special case of contiguous rows */
2509: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2510: /* loop over new rows determining lens and starting points */
2511: for (i = 0; i < nrows; i++) {
2512: kstart = ai[irow[i]];
2513: kend = kstart + ailen[irow[i]];
2514: starts[i] = kstart;
2515: for (k = kstart; k < kend; k++) {
2516: if (aj[k] >= first) {
2517: starts[i] = k;
2518: break;
2519: }
2520: }
2521: sum = 0;
2522: while (k < kend) {
2523: if (aj[k++] >= first + ncols) break;
2524: sum++;
2525: }
2526: lens[i] = sum;
2527: }
2528: /* create submatrix */
2529: if (scall == MAT_REUSE_MATRIX) {
2530: PetscInt n_cols, n_rows;
2531: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2532: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2533: PetscCall(MatZeroEntries(*B));
2534: C = *B;
2535: } else {
2536: PetscInt rbs, cbs;
2537: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2538: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2539: PetscCall(ISGetBlockSize(isrow, &rbs));
2540: PetscCall(ISGetBlockSize(iscol, &cbs));
2541: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2542: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2543: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2544: }
2545: c = (Mat_SeqAIJ *)C->data;
2547: /* loop over rows inserting into submatrix */
2548: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2549: j_new = c->j;
2550: i_new = c->i;
2551: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2552: for (i = 0; i < nrows; i++) {
2553: ii = starts[i];
2554: lensi = lens[i];
2555: if (lensi) {
2556: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2557: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2558: a_new += lensi;
2559: }
2560: i_new[i + 1] = i_new[i] + lensi;
2561: c->ilen[i] = lensi;
2562: }
2563: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2564: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2565: PetscCall(PetscFree2(lens, starts));
2566: } else {
2567: PetscCall(ISGetIndices(iscol, &icol));
2568: PetscCall(PetscCalloc1(oldcols, &smap));
2569: PetscCall(PetscMalloc1(1 + nrows, &lens));
2570: for (i = 0; i < ncols; i++) {
2571: 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);
2572: smap[icol[i]] = i + 1;
2573: }
2575: /* determine lens of each row */
2576: for (i = 0; i < nrows; i++) {
2577: kstart = ai[irow[i]];
2578: kend = kstart + a->ilen[irow[i]];
2579: lens[i] = 0;
2580: for (k = kstart; k < kend; k++) {
2581: if (smap[aj[k]]) lens[i]++;
2582: }
2583: }
2584: /* Create and fill new matrix */
2585: if (scall == MAT_REUSE_MATRIX) {
2586: PetscBool equal;
2588: c = (Mat_SeqAIJ *)((*B)->data);
2589: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2590: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2591: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2592: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2593: C = *B;
2594: } else {
2595: PetscInt rbs, cbs;
2596: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2597: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2598: PetscCall(ISGetBlockSize(isrow, &rbs));
2599: PetscCall(ISGetBlockSize(iscol, &cbs));
2600: if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2601: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2602: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2603: }
2604: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2606: c = (Mat_SeqAIJ *)C->data;
2607: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2608: for (i = 0; i < nrows; i++) {
2609: row = irow[i];
2610: kstart = ai[row];
2611: kend = kstart + a->ilen[row];
2612: mat_i = c->i[i];
2613: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2614: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2615: mat_ilen = c->ilen + i;
2616: for (k = kstart; k < kend; k++) {
2617: if ((tcol = smap[a->j[k]])) {
2618: *mat_j++ = tcol - 1;
2619: *mat_a++ = aa[k];
2620: (*mat_ilen)++;
2621: }
2622: }
2623: }
2624: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2625: /* Free work space */
2626: PetscCall(ISRestoreIndices(iscol, &icol));
2627: PetscCall(PetscFree(smap));
2628: PetscCall(PetscFree(lens));
2629: /* sort */
2630: for (i = 0; i < nrows; i++) {
2631: PetscInt ilen;
2633: mat_i = c->i[i];
2634: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2635: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2636: ilen = c->ilen[i];
2637: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2638: }
2639: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2640: }
2641: #if defined(PETSC_HAVE_DEVICE)
2642: PetscCall(MatBindToCPU(C, A->boundtocpu));
2643: #endif
2644: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2645: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2647: PetscCall(ISRestoreIndices(isrow, &irow));
2648: *B = C;
2649: PetscFunctionReturn(PETSC_SUCCESS);
2650: }
2652: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2653: {
2654: Mat B;
2656: PetscFunctionBegin;
2657: if (scall == MAT_INITIAL_MATRIX) {
2658: PetscCall(MatCreate(subComm, &B));
2659: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2660: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2661: PetscCall(MatSetType(B, MATSEQAIJ));
2662: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2663: *subMat = B;
2664: } else {
2665: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2666: }
2667: PetscFunctionReturn(PETSC_SUCCESS);
2668: }
2670: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2671: {
2672: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2673: Mat outA;
2674: PetscBool row_identity, col_identity;
2676: PetscFunctionBegin;
2677: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2679: PetscCall(ISIdentity(row, &row_identity));
2680: PetscCall(ISIdentity(col, &col_identity));
2682: outA = inA;
2683: outA->factortype = MAT_FACTOR_LU;
2684: PetscCall(PetscFree(inA->solvertype));
2685: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2687: PetscCall(PetscObjectReference((PetscObject)row));
2688: PetscCall(ISDestroy(&a->row));
2690: a->row = row;
2692: PetscCall(PetscObjectReference((PetscObject)col));
2693: PetscCall(ISDestroy(&a->col));
2695: a->col = col;
2697: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2698: PetscCall(ISDestroy(&a->icol));
2699: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2701: if (!a->solve_work) { /* this matrix may have been factored before */
2702: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2703: }
2705: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2706: if (row_identity && col_identity) {
2707: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2708: } else {
2709: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2710: }
2711: PetscFunctionReturn(PETSC_SUCCESS);
2712: }
2714: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2715: {
2716: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2717: PetscScalar *v;
2718: PetscBLASInt one = 1, bnz;
2720: PetscFunctionBegin;
2721: PetscCall(MatSeqAIJGetArray(inA, &v));
2722: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2723: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2724: PetscCall(PetscLogFlops(a->nz));
2725: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2726: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2727: PetscFunctionReturn(PETSC_SUCCESS);
2728: }
2730: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2731: {
2732: PetscInt i;
2734: PetscFunctionBegin;
2735: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2736: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2738: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2739: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2741: if (submatj->rbuf1) {
2742: PetscCall(PetscFree(submatj->rbuf1[0]));
2743: PetscCall(PetscFree(submatj->rbuf1));
2744: }
2746: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2747: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2748: PetscCall(PetscFree(submatj->pa));
2749: }
2751: #if defined(PETSC_USE_CTABLE)
2752: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2753: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2754: PetscCall(PetscFree(submatj->rmap_loc));
2755: #else
2756: PetscCall(PetscFree(submatj->rmap));
2757: #endif
2759: if (!submatj->allcolumns) {
2760: #if defined(PETSC_USE_CTABLE)
2761: PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2762: #else
2763: PetscCall(PetscFree(submatj->cmap));
2764: #endif
2765: }
2766: PetscCall(PetscFree(submatj->row2proc));
2768: PetscCall(PetscFree(submatj));
2769: PetscFunctionReturn(PETSC_SUCCESS);
2770: }
2772: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2773: {
2774: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2775: Mat_SubSppt *submatj = c->submatis1;
2777: PetscFunctionBegin;
2778: PetscCall((*submatj->destroy)(C));
2779: PetscCall(MatDestroySubMatrix_Private(submatj));
2780: PetscFunctionReturn(PETSC_SUCCESS);
2781: }
2783: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2784: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2785: {
2786: PetscInt i;
2787: Mat C;
2788: Mat_SeqAIJ *c;
2789: Mat_SubSppt *submatj;
2791: PetscFunctionBegin;
2792: for (i = 0; i < n; i++) {
2793: C = (*mat)[i];
2794: c = (Mat_SeqAIJ *)C->data;
2795: submatj = c->submatis1;
2796: if (submatj) {
2797: if (--((PetscObject)C)->refct <= 0) {
2798: PetscCall(PetscFree(C->factorprefix));
2799: PetscCall((*submatj->destroy)(C));
2800: PetscCall(MatDestroySubMatrix_Private(submatj));
2801: PetscCall(PetscFree(C->defaultvectype));
2802: PetscCall(PetscFree(C->defaultrandtype));
2803: PetscCall(PetscLayoutDestroy(&C->rmap));
2804: PetscCall(PetscLayoutDestroy(&C->cmap));
2805: PetscCall(PetscHeaderDestroy(&C));
2806: }
2807: } else {
2808: PetscCall(MatDestroy(&C));
2809: }
2810: }
2812: /* Destroy Dummy submatrices created for reuse */
2813: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2815: PetscCall(PetscFree(*mat));
2816: PetscFunctionReturn(PETSC_SUCCESS);
2817: }
2819: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2820: {
2821: PetscInt i;
2823: PetscFunctionBegin;
2824: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2826: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2827: PetscFunctionReturn(PETSC_SUCCESS);
2828: }
2830: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2831: {
2832: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2833: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2834: const PetscInt *idx;
2835: PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2836: PetscBT table;
2838: PetscFunctionBegin;
2839: m = A->rmap->n / bs;
2840: ai = a->i;
2841: aj = a->j;
2843: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2845: PetscCall(PetscMalloc1(m + 1, &nidx));
2846: PetscCall(PetscBTCreate(m, &table));
2848: for (i = 0; i < is_max; i++) {
2849: /* Initialize the two local arrays */
2850: isz = 0;
2851: PetscCall(PetscBTMemzero(m, table));
2853: /* Extract the indices, assume there can be duplicate entries */
2854: PetscCall(ISGetIndices(is[i], &idx));
2855: PetscCall(ISGetLocalSize(is[i], &n));
2857: if (bs > 1) {
2858: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2859: for (j = 0; j < n; ++j) {
2860: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2861: }
2862: PetscCall(ISRestoreIndices(is[i], &idx));
2863: PetscCall(ISDestroy(&is[i]));
2865: k = 0;
2866: for (j = 0; j < ov; j++) { /* for each overlap */
2867: n = isz;
2868: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2869: for (ll = 0; ll < bs; ll++) {
2870: row = bs * nidx[k] + ll;
2871: start = ai[row];
2872: end = ai[row + 1];
2873: for (l = start; l < end; l++) {
2874: val = aj[l] / bs;
2875: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2876: }
2877: }
2878: }
2879: }
2880: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2881: } else {
2882: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2883: for (j = 0; j < n; ++j) {
2884: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2885: }
2886: PetscCall(ISRestoreIndices(is[i], &idx));
2887: PetscCall(ISDestroy(&is[i]));
2889: k = 0;
2890: for (j = 0; j < ov; j++) { /* for each overlap */
2891: n = isz;
2892: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2893: row = nidx[k];
2894: start = ai[row];
2895: end = ai[row + 1];
2896: for (l = start; l < end; l++) {
2897: val = aj[l];
2898: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2899: }
2900: }
2901: }
2902: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2903: }
2904: }
2905: PetscCall(PetscBTDestroy(&table));
2906: PetscCall(PetscFree(nidx));
2907: PetscFunctionReturn(PETSC_SUCCESS);
2908: }
2910: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2911: {
2912: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2913: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2914: const PetscInt *row, *col;
2915: PetscInt *cnew, j, *lens;
2916: IS icolp, irowp;
2917: PetscInt *cwork = NULL;
2918: PetscScalar *vwork = NULL;
2920: PetscFunctionBegin;
2921: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2922: PetscCall(ISGetIndices(irowp, &row));
2923: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2924: PetscCall(ISGetIndices(icolp, &col));
2926: /* determine lengths of permuted rows */
2927: PetscCall(PetscMalloc1(m + 1, &lens));
2928: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2929: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2930: PetscCall(MatSetSizes(*B, m, n, m, n));
2931: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2932: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2933: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2934: PetscCall(PetscFree(lens));
2936: PetscCall(PetscMalloc1(n, &cnew));
2937: for (i = 0; i < m; i++) {
2938: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2939: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2940: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2941: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2942: }
2943: PetscCall(PetscFree(cnew));
2945: (*B)->assembled = PETSC_FALSE;
2947: #if defined(PETSC_HAVE_DEVICE)
2948: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2949: #endif
2950: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2951: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2952: PetscCall(ISRestoreIndices(irowp, &row));
2953: PetscCall(ISRestoreIndices(icolp, &col));
2954: PetscCall(ISDestroy(&irowp));
2955: PetscCall(ISDestroy(&icolp));
2956: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2957: PetscFunctionReturn(PETSC_SUCCESS);
2958: }
2960: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2961: {
2962: PetscFunctionBegin;
2963: /* If the two matrices have the same copy implementation, use fast copy. */
2964: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2965: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2966: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2967: const PetscScalar *aa;
2969: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2970: 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]);
2971: PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2972: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2973: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2974: } else {
2975: PetscCall(MatCopy_Basic(A, B, str));
2976: }
2977: PetscFunctionReturn(PETSC_SUCCESS);
2978: }
2980: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2981: {
2982: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2984: PetscFunctionBegin;
2985: *array = a->a;
2986: PetscFunctionReturn(PETSC_SUCCESS);
2987: }
2989: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2990: {
2991: PetscFunctionBegin;
2992: *array = NULL;
2993: PetscFunctionReturn(PETSC_SUCCESS);
2994: }
2996: /*
2997: Computes the number of nonzeros per row needed for preallocation when X and Y
2998: have different nonzero structure.
2999: */
3000: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
3001: {
3002: PetscInt i, j, k, nzx, nzy;
3004: PetscFunctionBegin;
3005: /* Set the number of nonzeros in the new matrix */
3006: for (i = 0; i < m; i++) {
3007: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
3008: nzx = xi[i + 1] - xi[i];
3009: nzy = yi[i + 1] - yi[i];
3010: nnz[i] = 0;
3011: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
3012: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
3013: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
3014: nnz[i]++;
3015: }
3016: for (; k < nzy; k++) nnz[i]++;
3017: }
3018: PetscFunctionReturn(PETSC_SUCCESS);
3019: }
3021: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3022: {
3023: PetscInt m = Y->rmap->N;
3024: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3025: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3027: PetscFunctionBegin;
3028: /* Set the number of nonzeros in the new matrix */
3029: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3030: PetscFunctionReturn(PETSC_SUCCESS);
3031: }
3033: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3034: {
3035: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3037: PetscFunctionBegin;
3038: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3039: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3040: if (e) {
3041: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3042: if (e) {
3043: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3044: if (e) str = SAME_NONZERO_PATTERN;
3045: }
3046: }
3047: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3048: }
3049: if (str == SAME_NONZERO_PATTERN) {
3050: const PetscScalar *xa;
3051: PetscScalar *ya, alpha = a;
3052: PetscBLASInt one = 1, bnz;
3054: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3055: PetscCall(MatSeqAIJGetArray(Y, &ya));
3056: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3057: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3058: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3059: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3060: PetscCall(PetscLogFlops(2.0 * bnz));
3061: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3062: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3063: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3064: PetscCall(MatAXPY_Basic(Y, a, X, str));
3065: } else {
3066: Mat B;
3067: PetscInt *nnz;
3068: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3069: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3070: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3071: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3072: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3073: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3074: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3075: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3076: PetscCall(MatHeaderMerge(Y, &B));
3077: PetscCall(MatSeqAIJCheckInode(Y));
3078: PetscCall(PetscFree(nnz));
3079: }
3080: PetscFunctionReturn(PETSC_SUCCESS);
3081: }
3083: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3084: {
3085: #if defined(PETSC_USE_COMPLEX)
3086: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3087: PetscInt i, nz;
3088: PetscScalar *a;
3090: PetscFunctionBegin;
3091: nz = aij->nz;
3092: PetscCall(MatSeqAIJGetArray(mat, &a));
3093: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3094: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3095: #else
3096: PetscFunctionBegin;
3097: #endif
3098: PetscFunctionReturn(PETSC_SUCCESS);
3099: }
3101: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3102: {
3103: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3104: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3105: PetscReal atmp;
3106: PetscScalar *x;
3107: const MatScalar *aa, *av;
3109: PetscFunctionBegin;
3110: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3111: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3112: aa = av;
3113: ai = a->i;
3114: aj = a->j;
3116: PetscCall(VecSet(v, 0.0));
3117: PetscCall(VecGetArrayWrite(v, &x));
3118: PetscCall(VecGetLocalSize(v, &n));
3119: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3120: for (i = 0; i < m; i++) {
3121: ncols = ai[1] - ai[0];
3122: ai++;
3123: for (j = 0; j < ncols; j++) {
3124: atmp = PetscAbsScalar(*aa);
3125: if (PetscAbsScalar(x[i]) < atmp) {
3126: x[i] = atmp;
3127: if (idx) idx[i] = *aj;
3128: }
3129: aa++;
3130: aj++;
3131: }
3132: }
3133: PetscCall(VecRestoreArrayWrite(v, &x));
3134: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3135: PetscFunctionReturn(PETSC_SUCCESS);
3136: }
3138: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3139: {
3140: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3141: PetscInt i, j, m = A->rmap->n, *ai, ncols, n;
3142: PetscScalar *x;
3143: const MatScalar *aa, *av;
3145: PetscFunctionBegin;
3146: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3147: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3148: aa = av;
3149: ai = a->i;
3151: PetscCall(VecSet(v, 0.0));
3152: PetscCall(VecGetArrayWrite(v, &x));
3153: PetscCall(VecGetLocalSize(v, &n));
3154: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3155: for (i = 0; i < m; i++) {
3156: ncols = ai[1] - ai[0];
3157: ai++;
3158: for (j = 0; j < ncols; j++) {
3159: x[i] += PetscAbsScalar(*aa);
3160: aa++;
3161: }
3162: }
3163: PetscCall(VecRestoreArrayWrite(v, &x));
3164: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3165: PetscFunctionReturn(PETSC_SUCCESS);
3166: }
3168: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3169: {
3170: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3171: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3172: PetscScalar *x;
3173: const MatScalar *aa, *av;
3175: PetscFunctionBegin;
3176: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3177: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3178: aa = av;
3179: ai = a->i;
3180: aj = a->j;
3182: PetscCall(VecSet(v, 0.0));
3183: PetscCall(VecGetArrayWrite(v, &x));
3184: PetscCall(VecGetLocalSize(v, &n));
3185: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3186: for (i = 0; i < m; i++) {
3187: ncols = ai[1] - ai[0];
3188: ai++;
3189: if (ncols == A->cmap->n) { /* row is dense */
3190: x[i] = *aa;
3191: if (idx) idx[i] = 0;
3192: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3193: x[i] = 0.0;
3194: if (idx) {
3195: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3196: if (aj[j] > j) {
3197: idx[i] = j;
3198: break;
3199: }
3200: }
3201: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3202: if (j == ncols && j < A->cmap->n) idx[i] = j;
3203: }
3204: }
3205: for (j = 0; j < ncols; j++) {
3206: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3207: x[i] = *aa;
3208: if (idx) idx[i] = *aj;
3209: }
3210: aa++;
3211: aj++;
3212: }
3213: }
3214: PetscCall(VecRestoreArrayWrite(v, &x));
3215: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3216: PetscFunctionReturn(PETSC_SUCCESS);
3217: }
3219: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3220: {
3221: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3222: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3223: PetscScalar *x;
3224: const MatScalar *aa, *av;
3226: PetscFunctionBegin;
3227: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3228: aa = av;
3229: ai = a->i;
3230: aj = a->j;
3232: PetscCall(VecSet(v, 0.0));
3233: PetscCall(VecGetArrayWrite(v, &x));
3234: PetscCall(VecGetLocalSize(v, &n));
3235: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3236: for (i = 0; i < m; i++) {
3237: ncols = ai[1] - ai[0];
3238: ai++;
3239: if (ncols == A->cmap->n) { /* row is dense */
3240: x[i] = *aa;
3241: if (idx) idx[i] = 0;
3242: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3243: x[i] = 0.0;
3244: if (idx) { /* find first implicit 0.0 in the row */
3245: for (j = 0; j < ncols; j++) {
3246: if (aj[j] > j) {
3247: idx[i] = j;
3248: break;
3249: }
3250: }
3251: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3252: if (j == ncols && j < A->cmap->n) idx[i] = j;
3253: }
3254: }
3255: for (j = 0; j < ncols; j++) {
3256: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3257: x[i] = *aa;
3258: if (idx) idx[i] = *aj;
3259: }
3260: aa++;
3261: aj++;
3262: }
3263: }
3264: PetscCall(VecRestoreArrayWrite(v, &x));
3265: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3266: PetscFunctionReturn(PETSC_SUCCESS);
3267: }
3269: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3270: {
3271: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3272: PetscInt i, j, m = A->rmap->n, ncols, n;
3273: const PetscInt *ai, *aj;
3274: PetscScalar *x;
3275: const MatScalar *aa, *av;
3277: PetscFunctionBegin;
3278: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3279: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3280: aa = av;
3281: ai = a->i;
3282: aj = a->j;
3284: PetscCall(VecSet(v, 0.0));
3285: PetscCall(VecGetArrayWrite(v, &x));
3286: PetscCall(VecGetLocalSize(v, &n));
3287: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3288: for (i = 0; i < m; i++) {
3289: ncols = ai[1] - ai[0];
3290: ai++;
3291: if (ncols == A->cmap->n) { /* row is dense */
3292: x[i] = *aa;
3293: if (idx) idx[i] = 0;
3294: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3295: x[i] = 0.0;
3296: if (idx) { /* find first implicit 0.0 in the row */
3297: for (j = 0; j < ncols; j++) {
3298: if (aj[j] > j) {
3299: idx[i] = j;
3300: break;
3301: }
3302: }
3303: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3304: if (j == ncols && j < A->cmap->n) idx[i] = j;
3305: }
3306: }
3307: for (j = 0; j < ncols; j++) {
3308: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3309: x[i] = *aa;
3310: if (idx) idx[i] = *aj;
3311: }
3312: aa++;
3313: aj++;
3314: }
3315: }
3316: PetscCall(VecRestoreArrayWrite(v, &x));
3317: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3318: PetscFunctionReturn(PETSC_SUCCESS);
3319: }
3321: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3322: {
3323: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3324: PetscInt i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3325: MatScalar *diag, work[25], *v_work;
3326: const PetscReal shift = 0.0;
3327: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3329: PetscFunctionBegin;
3330: allowzeropivot = PetscNot(A->erroriffailure);
3331: if (a->ibdiagvalid) {
3332: if (values) *values = a->ibdiag;
3333: PetscFunctionReturn(PETSC_SUCCESS);
3334: }
3335: PetscCall(MatMarkDiagonal_SeqAIJ(A));
3336: if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3337: diag = a->ibdiag;
3338: if (values) *values = a->ibdiag;
3339: /* factor and invert each block */
3340: switch (bs) {
3341: case 1:
3342: for (i = 0; i < mbs; i++) {
3343: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3344: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3345: if (allowzeropivot) {
3346: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3347: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3348: A->factorerror_zeropivot_row = i;
3349: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3350: } 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);
3351: }
3352: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3353: }
3354: break;
3355: case 2:
3356: for (i = 0; i < mbs; i++) {
3357: ij[0] = 2 * i;
3358: ij[1] = 2 * i + 1;
3359: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3360: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3361: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3362: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3363: diag += 4;
3364: }
3365: break;
3366: case 3:
3367: for (i = 0; i < mbs; i++) {
3368: ij[0] = 3 * i;
3369: ij[1] = 3 * i + 1;
3370: ij[2] = 3 * i + 2;
3371: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3372: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3373: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3374: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3375: diag += 9;
3376: }
3377: break;
3378: case 4:
3379: for (i = 0; i < mbs; i++) {
3380: ij[0] = 4 * i;
3381: ij[1] = 4 * i + 1;
3382: ij[2] = 4 * i + 2;
3383: ij[3] = 4 * i + 3;
3384: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3385: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3386: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3387: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3388: diag += 16;
3389: }
3390: break;
3391: case 5:
3392: for (i = 0; i < mbs; i++) {
3393: ij[0] = 5 * i;
3394: ij[1] = 5 * i + 1;
3395: ij[2] = 5 * i + 2;
3396: ij[3] = 5 * i + 3;
3397: ij[4] = 5 * i + 4;
3398: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3399: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3400: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3401: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3402: diag += 25;
3403: }
3404: break;
3405: case 6:
3406: for (i = 0; i < mbs; i++) {
3407: ij[0] = 6 * i;
3408: ij[1] = 6 * i + 1;
3409: ij[2] = 6 * i + 2;
3410: ij[3] = 6 * i + 3;
3411: ij[4] = 6 * i + 4;
3412: ij[5] = 6 * i + 5;
3413: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3414: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3415: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3416: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3417: diag += 36;
3418: }
3419: break;
3420: case 7:
3421: for (i = 0; i < mbs; i++) {
3422: ij[0] = 7 * i;
3423: ij[1] = 7 * i + 1;
3424: ij[2] = 7 * i + 2;
3425: ij[3] = 7 * i + 3;
3426: ij[4] = 7 * i + 4;
3427: ij[5] = 7 * i + 5;
3428: ij[6] = 7 * i + 6;
3429: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3430: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3431: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3432: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3433: diag += 49;
3434: }
3435: break;
3436: default:
3437: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3438: for (i = 0; i < mbs; i++) {
3439: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3440: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3441: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3442: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3443: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3444: diag += bs2;
3445: }
3446: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3447: }
3448: a->ibdiagvalid = PETSC_TRUE;
3449: PetscFunctionReturn(PETSC_SUCCESS);
3450: }
3452: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3453: {
3454: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3455: PetscScalar a, *aa;
3456: PetscInt m, n, i, j, col;
3458: PetscFunctionBegin;
3459: if (!x->assembled) {
3460: PetscCall(MatGetSize(x, &m, &n));
3461: for (i = 0; i < m; i++) {
3462: for (j = 0; j < aij->imax[i]; j++) {
3463: PetscCall(PetscRandomGetValue(rctx, &a));
3464: col = (PetscInt)(n * PetscRealPart(a));
3465: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3466: }
3467: }
3468: } else {
3469: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3470: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3471: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3472: }
3473: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3474: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3475: PetscFunctionReturn(PETSC_SUCCESS);
3476: }
3478: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3479: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3480: {
3481: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3482: PetscScalar a;
3483: PetscInt m, n, i, j, col, nskip;
3485: PetscFunctionBegin;
3486: nskip = high - low;
3487: PetscCall(MatGetSize(x, &m, &n));
3488: n -= nskip; /* shrink number of columns where nonzeros can be set */
3489: for (i = 0; i < m; i++) {
3490: for (j = 0; j < aij->imax[i]; j++) {
3491: PetscCall(PetscRandomGetValue(rctx, &a));
3492: col = (PetscInt)(n * PetscRealPart(a));
3493: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3494: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3495: }
3496: }
3497: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3498: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3499: PetscFunctionReturn(PETSC_SUCCESS);
3500: }
3502: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3503: MatGetRow_SeqAIJ,
3504: MatRestoreRow_SeqAIJ,
3505: MatMult_SeqAIJ,
3506: /* 4*/ MatMultAdd_SeqAIJ,
3507: MatMultTranspose_SeqAIJ,
3508: MatMultTransposeAdd_SeqAIJ,
3509: NULL,
3510: NULL,
3511: NULL,
3512: /* 10*/ NULL,
3513: MatLUFactor_SeqAIJ,
3514: NULL,
3515: MatSOR_SeqAIJ,
3516: MatTranspose_SeqAIJ,
3517: /*1 5*/ MatGetInfo_SeqAIJ,
3518: MatEqual_SeqAIJ,
3519: MatGetDiagonal_SeqAIJ,
3520: MatDiagonalScale_SeqAIJ,
3521: MatNorm_SeqAIJ,
3522: /* 20*/ NULL,
3523: MatAssemblyEnd_SeqAIJ,
3524: MatSetOption_SeqAIJ,
3525: MatZeroEntries_SeqAIJ,
3526: /* 24*/ MatZeroRows_SeqAIJ,
3527: NULL,
3528: NULL,
3529: NULL,
3530: NULL,
3531: /* 29*/ MatSetUp_Seq_Hash,
3532: NULL,
3533: NULL,
3534: NULL,
3535: NULL,
3536: /* 34*/ MatDuplicate_SeqAIJ,
3537: NULL,
3538: NULL,
3539: MatILUFactor_SeqAIJ,
3540: NULL,
3541: /* 39*/ MatAXPY_SeqAIJ,
3542: MatCreateSubMatrices_SeqAIJ,
3543: MatIncreaseOverlap_SeqAIJ,
3544: MatGetValues_SeqAIJ,
3545: MatCopy_SeqAIJ,
3546: /* 44*/ MatGetRowMax_SeqAIJ,
3547: MatScale_SeqAIJ,
3548: MatShift_SeqAIJ,
3549: MatDiagonalSet_SeqAIJ,
3550: MatZeroRowsColumns_SeqAIJ,
3551: /* 49*/ MatSetRandom_SeqAIJ,
3552: MatGetRowIJ_SeqAIJ,
3553: MatRestoreRowIJ_SeqAIJ,
3554: MatGetColumnIJ_SeqAIJ,
3555: MatRestoreColumnIJ_SeqAIJ,
3556: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3557: NULL,
3558: NULL,
3559: MatPermute_SeqAIJ,
3560: NULL,
3561: /* 59*/ NULL,
3562: MatDestroy_SeqAIJ,
3563: MatView_SeqAIJ,
3564: NULL,
3565: NULL,
3566: /* 64*/ NULL,
3567: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3568: NULL,
3569: NULL,
3570: NULL,
3571: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3572: MatGetRowMinAbs_SeqAIJ,
3573: NULL,
3574: NULL,
3575: NULL,
3576: /* 74*/ NULL,
3577: MatFDColoringApply_AIJ,
3578: NULL,
3579: NULL,
3580: NULL,
3581: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3582: NULL,
3583: NULL,
3584: NULL,
3585: MatLoad_SeqAIJ,
3586: /* 84*/ MatIsSymmetric_SeqAIJ,
3587: MatIsHermitian_SeqAIJ,
3588: NULL,
3589: NULL,
3590: NULL,
3591: /* 89*/ NULL,
3592: NULL,
3593: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3594: NULL,
3595: NULL,
3596: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3597: NULL,
3598: NULL,
3599: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3600: NULL,
3601: /* 99*/ MatProductSetFromOptions_SeqAIJ,
3602: NULL,
3603: NULL,
3604: MatConjugate_SeqAIJ,
3605: NULL,
3606: /*104*/ MatSetValuesRow_SeqAIJ,
3607: MatRealPart_SeqAIJ,
3608: MatImaginaryPart_SeqAIJ,
3609: NULL,
3610: NULL,
3611: /*109*/ MatMatSolve_SeqAIJ,
3612: NULL,
3613: MatGetRowMin_SeqAIJ,
3614: NULL,
3615: MatMissingDiagonal_SeqAIJ,
3616: /*114*/ NULL,
3617: NULL,
3618: NULL,
3619: NULL,
3620: NULL,
3621: /*119*/ NULL,
3622: NULL,
3623: NULL,
3624: NULL,
3625: MatGetMultiProcBlock_SeqAIJ,
3626: /*124*/ MatFindNonzeroRows_SeqAIJ,
3627: MatGetColumnReductions_SeqAIJ,
3628: MatInvertBlockDiagonal_SeqAIJ,
3629: MatInvertVariableBlockDiagonal_SeqAIJ,
3630: NULL,
3631: /*129*/ NULL,
3632: NULL,
3633: NULL,
3634: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3635: MatTransposeColoringCreate_SeqAIJ,
3636: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3637: MatTransColoringApplyDenToSp_SeqAIJ,
3638: NULL,
3639: NULL,
3640: MatRARtNumeric_SeqAIJ_SeqAIJ,
3641: /*139*/ NULL,
3642: NULL,
3643: NULL,
3644: MatFDColoringSetUp_SeqXAIJ,
3645: MatFindOffBlockDiagonalEntries_SeqAIJ,
3646: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3647: /*145*/ MatDestroySubMatrices_SeqAIJ,
3648: NULL,
3649: NULL,
3650: MatCreateGraph_Simple_AIJ,
3651: NULL,
3652: /*150*/ MatTransposeSymbolic_SeqAIJ,
3653: MatEliminateZeros_SeqAIJ,
3654: MatGetRowSumAbs_SeqAIJ};
3656: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3657: {
3658: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3659: PetscInt i, nz, n;
3661: PetscFunctionBegin;
3662: nz = aij->maxnz;
3663: n = mat->rmap->n;
3664: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3665: aij->nz = nz;
3666: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3667: PetscFunctionReturn(PETSC_SUCCESS);
3668: }
3670: /*
3671: * Given a sparse matrix with global column indices, compact it by using a local column space.
3672: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3673: */
3674: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3675: {
3676: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3677: PetscHMapI gid1_lid1;
3678: PetscHashIter tpos;
3679: PetscInt gid, lid, i, ec, nz = aij->nz;
3680: PetscInt *garray, *jj = aij->j;
3682: PetscFunctionBegin;
3684: PetscAssertPointer(mapping, 2);
3685: /* use a table */
3686: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3687: ec = 0;
3688: for (i = 0; i < nz; i++) {
3689: PetscInt data, gid1 = jj[i] + 1;
3690: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3691: if (!data) {
3692: /* one based table */
3693: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3694: }
3695: }
3696: /* form array of columns we need */
3697: PetscCall(PetscMalloc1(ec, &garray));
3698: PetscHashIterBegin(gid1_lid1, tpos);
3699: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3700: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3701: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3702: PetscHashIterNext(gid1_lid1, tpos);
3703: gid--;
3704: lid--;
3705: garray[lid] = gid;
3706: }
3707: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3708: PetscCall(PetscHMapIClear(gid1_lid1));
3709: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3710: /* compact out the extra columns in B */
3711: for (i = 0; i < nz; i++) {
3712: PetscInt gid1 = jj[i] + 1;
3713: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3714: lid--;
3715: jj[i] = lid;
3716: }
3717: PetscCall(PetscLayoutDestroy(&mat->cmap));
3718: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3719: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3720: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3721: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3722: PetscFunctionReturn(PETSC_SUCCESS);
3723: }
3725: /*@
3726: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3727: in the matrix.
3729: Input Parameters:
3730: + mat - the `MATSEQAIJ` matrix
3731: - indices - the column indices
3733: Level: advanced
3735: Notes:
3736: This can be called if you have precomputed the nonzero structure of the
3737: matrix and want to provide it to the matrix object to improve the performance
3738: of the `MatSetValues()` operation.
3740: You MUST have set the correct numbers of nonzeros per row in the call to
3741: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3743: MUST be called before any calls to `MatSetValues()`
3745: The indices should start with zero, not one.
3747: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3748: @*/
3749: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3750: {
3751: PetscFunctionBegin;
3753: PetscAssertPointer(indices, 2);
3754: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3755: PetscFunctionReturn(PETSC_SUCCESS);
3756: }
3758: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3759: {
3760: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3761: size_t nz = aij->i[mat->rmap->n];
3763: PetscFunctionBegin;
3764: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3766: /* allocate space for values if not already there */
3767: if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }
3769: /* copy values over */
3770: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3771: PetscFunctionReturn(PETSC_SUCCESS);
3772: }
3774: /*@
3775: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3776: nonlinear portion.
3778: Logically Collect
3780: Input Parameter:
3781: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3783: Level: advanced
3785: Example Usage:
3786: .vb
3787: Using SNES
3788: Create Jacobian matrix
3789: Set linear terms into matrix
3790: Apply boundary conditions to matrix, at this time matrix must have
3791: final nonzero structure (i.e. setting the nonlinear terms and applying
3792: boundary conditions again will not change the nonzero structure
3793: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3794: MatStoreValues(mat);
3795: Call SNESSetJacobian() with matrix
3796: In your Jacobian routine
3797: MatRetrieveValues(mat);
3798: Set nonlinear terms in matrix
3800: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3801: // build linear portion of Jacobian
3802: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3803: MatStoreValues(mat);
3804: loop over nonlinear iterations
3805: MatRetrieveValues(mat);
3806: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3807: // call MatAssemblyBegin/End() on matrix
3808: Solve linear system with Jacobian
3809: endloop
3810: .ve
3812: Notes:
3813: Matrix must already be assembled before calling this routine
3814: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3815: calling this routine.
3817: When this is called multiple times it overwrites the previous set of stored values
3818: and does not allocated additional space.
3820: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3821: @*/
3822: PetscErrorCode MatStoreValues(Mat mat)
3823: {
3824: PetscFunctionBegin;
3826: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3827: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3828: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3829: PetscFunctionReturn(PETSC_SUCCESS);
3830: }
3832: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3833: {
3834: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3835: PetscInt nz = aij->i[mat->rmap->n];
3837: PetscFunctionBegin;
3838: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3839: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3840: /* copy values over */
3841: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3842: PetscFunctionReturn(PETSC_SUCCESS);
3843: }
3845: /*@
3846: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3848: Logically Collect
3850: Input Parameter:
3851: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3853: Level: advanced
3855: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3856: @*/
3857: PetscErrorCode MatRetrieveValues(Mat mat)
3858: {
3859: PetscFunctionBegin;
3861: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3862: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3863: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3864: PetscFunctionReturn(PETSC_SUCCESS);
3865: }
3867: /*@C
3868: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3869: (the default parallel PETSc format). For good matrix assembly performance
3870: the user should preallocate the matrix storage by setting the parameter `nz`
3871: (or the array `nnz`).
3873: Collective
3875: Input Parameters:
3876: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3877: . m - number of rows
3878: . n - number of columns
3879: . nz - number of nonzeros per row (same for all rows)
3880: - nnz - array containing the number of nonzeros in the various rows
3881: (possibly different for each row) or NULL
3883: Output Parameter:
3884: . A - the matrix
3886: Options Database Keys:
3887: + -mat_no_inode - Do not use inodes
3888: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3890: Level: intermediate
3892: Notes:
3893: It is recommend to use `MatCreateFromOptions()` instead of this routine
3895: If `nnz` is given then `nz` is ignored
3897: The `MATSEQAIJ` format, also called
3898: compressed row storage, is fully compatible with standard Fortran
3899: storage. That is, the stored row and column indices can begin at
3900: either one (as in Fortran) or zero.
3902: Specify the preallocated storage with either `nz` or `nnz` (not both).
3903: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3904: allocation.
3906: By default, this format uses inodes (identical nodes) when possible, to
3907: improve numerical efficiency of matrix-vector products and solves. We
3908: search for consecutive rows with the same nonzero structure, thereby
3909: reusing matrix information to achieve increased efficiency.
3911: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3912: @*/
3913: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3914: {
3915: PetscFunctionBegin;
3916: PetscCall(MatCreate(comm, A));
3917: PetscCall(MatSetSizes(*A, m, n, m, n));
3918: PetscCall(MatSetType(*A, MATSEQAIJ));
3919: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3920: PetscFunctionReturn(PETSC_SUCCESS);
3921: }
3923: /*@C
3924: MatSeqAIJSetPreallocation - For good matrix assembly performance
3925: the user should preallocate the matrix storage by setting the parameter nz
3926: (or the array nnz). By setting these parameters accurately, performance
3927: during matrix assembly can be increased by more than a factor of 50.
3929: Collective
3931: Input Parameters:
3932: + B - The matrix
3933: . nz - number of nonzeros per row (same for all rows)
3934: - nnz - array containing the number of nonzeros in the various rows
3935: (possibly different for each row) or NULL
3937: Options Database Keys:
3938: + -mat_no_inode - Do not use inodes
3939: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3941: Level: intermediate
3943: Notes:
3944: If `nnz` is given then `nz` is ignored
3946: The `MATSEQAIJ` format also called
3947: compressed row storage, is fully compatible with standard Fortran
3948: storage. That is, the stored row and column indices can begin at
3949: either one (as in Fortran) or zero. See the users' manual for details.
3951: Specify the preallocated storage with either `nz` or `nnz` (not both).
3952: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3953: allocation.
3955: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3956: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3957: You can also run with the option -info and look for messages with the string
3958: malloc in them to see if additional memory allocation was needed.
3960: Developer Notes:
3961: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3962: entries or columns indices
3964: By default, this format uses inodes (identical nodes) when possible, to
3965: improve numerical efficiency of matrix-vector products and solves. We
3966: search for consecutive rows with the same nonzero structure, thereby
3967: reusing matrix information to achieve increased efficiency.
3969: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3970: `MatSeqAIJSetTotalPreallocation()`
3971: @*/
3972: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3973: {
3974: PetscFunctionBegin;
3977: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3978: PetscFunctionReturn(PETSC_SUCCESS);
3979: }
3981: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3982: {
3983: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3984: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3985: PetscInt i;
3987: PetscFunctionBegin;
3988: if (B->hash_active) {
3989: B->ops[0] = b->cops;
3990: PetscCall(PetscHMapIJVDestroy(&b->ht));
3991: PetscCall(PetscFree(b->dnz));
3992: B->hash_active = PETSC_FALSE;
3993: }
3994: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3995: if (nz == MAT_SKIP_ALLOCATION) {
3996: skipallocation = PETSC_TRUE;
3997: nz = 0;
3998: }
3999: PetscCall(PetscLayoutSetUp(B->rmap));
4000: PetscCall(PetscLayoutSetUp(B->cmap));
4002: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
4003: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
4004: if (PetscUnlikelyDebug(nnz)) {
4005: for (i = 0; i < B->rmap->n; i++) {
4006: 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]);
4007: 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);
4008: }
4009: }
4011: B->preallocated = PETSC_TRUE;
4012: if (!skipallocation) {
4013: if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
4014: if (!b->ilen) {
4015: /* b->ilen will count nonzeros in each row so far. */
4016: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
4017: } else {
4018: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
4019: }
4020: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
4021: if (!nnz) {
4022: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4023: else if (nz < 0) nz = 1;
4024: nz = PetscMin(nz, B->cmap->n);
4025: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
4026: PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
4027: } else {
4028: PetscInt64 nz64 = 0;
4029: for (i = 0; i < B->rmap->n; i++) {
4030: b->imax[i] = nnz[i];
4031: nz64 += nnz[i];
4032: }
4033: PetscCall(PetscIntCast(nz64, &nz));
4034: }
4036: /* allocate the matrix space */
4037: /* FIXME: should B's old memory be unlogged? */
4038: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4039: if (B->structure_only) {
4040: PetscCall(PetscMalloc1(nz, &b->j));
4041: PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i));
4042: } else {
4043: PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i));
4044: }
4045: b->i[0] = 0;
4046: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4047: if (B->structure_only) {
4048: b->singlemalloc = PETSC_FALSE;
4049: b->free_a = PETSC_FALSE;
4050: } else {
4051: b->singlemalloc = PETSC_TRUE;
4052: b->free_a = PETSC_TRUE;
4053: }
4054: b->free_ij = PETSC_TRUE;
4055: } else {
4056: b->free_a = PETSC_FALSE;
4057: b->free_ij = PETSC_FALSE;
4058: }
4060: if (b->ipre && nnz != b->ipre && b->imax) {
4061: /* reserve user-requested sparsity */
4062: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4063: }
4065: b->nz = 0;
4066: b->maxnz = nz;
4067: B->info.nz_unneeded = (double)b->maxnz;
4068: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4069: B->was_assembled = PETSC_FALSE;
4070: B->assembled = PETSC_FALSE;
4071: /* We simply deem preallocation has changed nonzero state. Updating the state
4072: will give clients (like AIJKokkos) a chance to know something has happened.
4073: */
4074: B->nonzerostate++;
4075: PetscFunctionReturn(PETSC_SUCCESS);
4076: }
4078: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4079: {
4080: Mat_SeqAIJ *a;
4081: PetscInt i;
4082: PetscBool skipreset;
4084: PetscFunctionBegin;
4087: /* Check local size. If zero, then return */
4088: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
4090: a = (Mat_SeqAIJ *)A->data;
4091: /* if no saved info, we error out */
4092: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
4094: PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");
4096: PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4097: if (!skipreset) {
4098: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4099: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4100: a->i[0] = 0;
4101: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4102: A->preallocated = PETSC_TRUE;
4103: a->nz = 0;
4104: a->maxnz = a->i[A->rmap->n];
4105: A->info.nz_unneeded = (double)a->maxnz;
4106: A->was_assembled = PETSC_FALSE;
4107: A->assembled = PETSC_FALSE;
4108: }
4109: PetscFunctionReturn(PETSC_SUCCESS);
4110: }
4112: /*@
4113: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4115: Input Parameters:
4116: + B - the matrix
4117: . i - the indices into `j` for the start of each row (indices start with zero)
4118: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4119: - v - optional values in the matrix, use `NULL` if not provided
4121: Level: developer
4123: Notes:
4124: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4126: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4127: structure will be the union of all the previous nonzero structures.
4129: Developer Notes:
4130: 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
4131: then just copies the `v` values directly with `PetscMemcpy()`.
4133: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4135: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4136: @*/
4137: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4138: {
4139: PetscFunctionBegin;
4142: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4143: PetscFunctionReturn(PETSC_SUCCESS);
4144: }
4146: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4147: {
4148: PetscInt i;
4149: PetscInt m, n;
4150: PetscInt nz;
4151: PetscInt *nnz;
4153: PetscFunctionBegin;
4154: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4156: PetscCall(PetscLayoutSetUp(B->rmap));
4157: PetscCall(PetscLayoutSetUp(B->cmap));
4159: PetscCall(MatGetSize(B, &m, &n));
4160: PetscCall(PetscMalloc1(m + 1, &nnz));
4161: for (i = 0; i < m; i++) {
4162: nz = Ii[i + 1] - Ii[i];
4163: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4164: nnz[i] = nz;
4165: }
4166: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4167: PetscCall(PetscFree(nnz));
4169: 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));
4171: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4172: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4174: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4175: PetscFunctionReturn(PETSC_SUCCESS);
4176: }
4178: /*@
4179: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4181: Input Parameters:
4182: + A - left-hand side matrix
4183: . B - right-hand side matrix
4184: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4186: Output Parameter:
4187: . C - Kronecker product of `A` and `B`
4189: Level: intermediate
4191: Note:
4192: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4194: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4195: @*/
4196: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4197: {
4198: PetscFunctionBegin;
4203: PetscAssertPointer(C, 4);
4204: if (reuse == MAT_REUSE_MATRIX) {
4207: }
4208: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4209: PetscFunctionReturn(PETSC_SUCCESS);
4210: }
4212: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4213: {
4214: Mat newmat;
4215: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4216: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4217: PetscScalar *v;
4218: const PetscScalar *aa, *ba;
4219: 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;
4220: PetscBool flg;
4222: PetscFunctionBegin;
4223: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4224: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4225: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4226: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4227: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4228: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4229: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4230: if (reuse == MAT_INITIAL_MATRIX) {
4231: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4232: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4233: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4234: PetscCall(MatSetType(newmat, MATAIJ));
4235: i[0] = 0;
4236: for (m = 0; m < am; ++m) {
4237: for (p = 0; p < bm; ++p) {
4238: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4239: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4240: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4241: }
4242: }
4243: }
4244: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4245: *C = newmat;
4246: PetscCall(PetscFree2(i, j));
4247: nnz = 0;
4248: }
4249: PetscCall(MatSeqAIJGetArray(*C, &v));
4250: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4251: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4252: for (m = 0; m < am; ++m) {
4253: for (p = 0; p < bm; ++p) {
4254: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4255: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4256: }
4257: }
4258: }
4259: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4260: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4261: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4262: PetscFunctionReturn(PETSC_SUCCESS);
4263: }
4265: #include <../src/mat/impls/dense/seq/dense.h>
4266: #include <petsc/private/kernels/petscaxpy.h>
4268: /*
4269: Computes (B'*A')' since computing B*A directly is untenable
4271: n p p
4272: [ ] [ ] [ ]
4273: m [ A ] * n [ B ] = m [ C ]
4274: [ ] [ ] [ ]
4276: */
4277: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4278: {
4279: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4280: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4281: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4282: PetscInt i, j, n, m, q, p;
4283: const PetscInt *ii, *idx;
4284: const PetscScalar *b, *a, *a_q;
4285: PetscScalar *c, *c_q;
4286: PetscInt clda = sub_c->lda;
4287: PetscInt alda = sub_a->lda;
4289: PetscFunctionBegin;
4290: m = A->rmap->n;
4291: n = A->cmap->n;
4292: p = B->cmap->n;
4293: a = sub_a->v;
4294: b = sub_b->a;
4295: c = sub_c->v;
4296: if (clda == m) {
4297: PetscCall(PetscArrayzero(c, m * p));
4298: } else {
4299: for (j = 0; j < p; j++)
4300: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4301: }
4302: ii = sub_b->i;
4303: idx = sub_b->j;
4304: for (i = 0; i < n; i++) {
4305: q = ii[i + 1] - ii[i];
4306: while (q-- > 0) {
4307: c_q = c + clda * (*idx);
4308: a_q = a + alda * i;
4309: PetscKernelAXPY(c_q, *b, a_q, m);
4310: idx++;
4311: b++;
4312: }
4313: }
4314: PetscFunctionReturn(PETSC_SUCCESS);
4315: }
4317: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4318: {
4319: PetscInt m = A->rmap->n, n = B->cmap->n;
4320: PetscBool cisdense;
4322: PetscFunctionBegin;
4323: 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);
4324: PetscCall(MatSetSizes(C, m, n, m, n));
4325: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4326: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4327: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4328: PetscCall(MatSetUp(C));
4330: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4331: PetscFunctionReturn(PETSC_SUCCESS);
4332: }
4334: /*MC
4335: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4336: based on compressed sparse row format.
4338: Options Database Key:
4339: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4341: Level: beginner
4343: Notes:
4344: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4345: in this case the values associated with the rows and columns one passes in are set to zero
4346: in the matrix
4348: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4349: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4351: Developer Note:
4352: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4354: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4355: M*/
4357: /*MC
4358: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4360: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4361: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4362: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4363: for communicators controlling multiple processes. It is recommended that you call both of
4364: the above preallocation routines for simplicity.
4366: Options Database Key:
4367: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4369: Level: beginner
4371: Note:
4372: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4373: enough exist.
4375: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4376: M*/
4378: /*MC
4379: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4381: Options Database Key:
4382: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4384: Level: beginner
4386: Note:
4387: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4388: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4389: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4390: for communicators controlling multiple processes. It is recommended that you call both of
4391: the above preallocation routines for simplicity.
4393: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4394: M*/
4396: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4397: #if defined(PETSC_HAVE_ELEMENTAL)
4398: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4399: #endif
4400: #if defined(PETSC_HAVE_SCALAPACK)
4401: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4402: #endif
4403: #if defined(PETSC_HAVE_HYPRE)
4404: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4405: #endif
4407: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4408: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4409: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4411: /*@C
4412: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4414: Not Collective
4416: Input Parameter:
4417: . A - a `MATSEQAIJ` matrix
4419: Output Parameter:
4420: . array - pointer to the data
4422: Level: intermediate
4424: Fortran Notes:
4425: `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`
4427: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4428: @*/
4429: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array)
4430: {
4431: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4433: PetscFunctionBegin;
4434: if (aij->ops->getarray) {
4435: PetscCall((*aij->ops->getarray)(A, array));
4436: } else {
4437: *array = aij->a;
4438: }
4439: PetscFunctionReturn(PETSC_SUCCESS);
4440: }
4442: /*@C
4443: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4445: Not Collective
4447: Input Parameters:
4448: + A - a `MATSEQAIJ` matrix
4449: - array - pointer to the data
4451: Level: intermediate
4453: Fortran Notes:
4454: `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`
4456: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4457: @*/
4458: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array)
4459: {
4460: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4462: PetscFunctionBegin;
4463: if (aij->ops->restorearray) {
4464: PetscCall((*aij->ops->restorearray)(A, array));
4465: } else {
4466: *array = NULL;
4467: }
4468: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4469: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4470: PetscFunctionReturn(PETSC_SUCCESS);
4471: }
4473: /*@C
4474: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4476: Not Collective; No Fortran Support
4478: Input Parameter:
4479: . A - a `MATSEQAIJ` matrix
4481: Output Parameter:
4482: . array - pointer to the data
4484: Level: intermediate
4486: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4487: @*/
4488: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array)
4489: {
4490: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4492: PetscFunctionBegin;
4493: if (aij->ops->getarrayread) {
4494: PetscCall((*aij->ops->getarrayread)(A, array));
4495: } else {
4496: *array = aij->a;
4497: }
4498: PetscFunctionReturn(PETSC_SUCCESS);
4499: }
4501: /*@C
4502: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4504: Not Collective; No Fortran Support
4506: Input Parameter:
4507: . A - a `MATSEQAIJ` matrix
4509: Output Parameter:
4510: . array - pointer to the data
4512: Level: intermediate
4514: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4515: @*/
4516: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array)
4517: {
4518: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4520: PetscFunctionBegin;
4521: if (aij->ops->restorearrayread) {
4522: PetscCall((*aij->ops->restorearrayread)(A, array));
4523: } else {
4524: *array = NULL;
4525: }
4526: PetscFunctionReturn(PETSC_SUCCESS);
4527: }
4529: /*@C
4530: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4532: Not Collective; No Fortran Support
4534: Input Parameter:
4535: . A - a `MATSEQAIJ` matrix
4537: Output Parameter:
4538: . array - pointer to the data
4540: Level: intermediate
4542: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4543: @*/
4544: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array)
4545: {
4546: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4548: PetscFunctionBegin;
4549: if (aij->ops->getarraywrite) {
4550: PetscCall((*aij->ops->getarraywrite)(A, array));
4551: } else {
4552: *array = aij->a;
4553: }
4554: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4555: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4556: PetscFunctionReturn(PETSC_SUCCESS);
4557: }
4559: /*@C
4560: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4562: Not Collective; No Fortran Support
4564: Input Parameter:
4565: . A - a MATSEQAIJ matrix
4567: Output Parameter:
4568: . array - pointer to the data
4570: Level: intermediate
4572: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4573: @*/
4574: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array)
4575: {
4576: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4578: PetscFunctionBegin;
4579: if (aij->ops->restorearraywrite) {
4580: PetscCall((*aij->ops->restorearraywrite)(A, array));
4581: } else {
4582: *array = NULL;
4583: }
4584: PetscFunctionReturn(PETSC_SUCCESS);
4585: }
4587: /*@C
4588: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4590: Not Collective; No Fortran Support
4592: Input Parameter:
4593: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4595: Output Parameters:
4596: + i - row map array of the matrix
4597: . j - column index array of the matrix
4598: . a - data array of the matrix
4599: - mtype - memory type of the arrays
4601: Level: developer
4603: Notes:
4604: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4605: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4607: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4608: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4610: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4611: @*/
4612: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
4613: {
4614: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4616: PetscFunctionBegin;
4617: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4618: if (aij->ops->getcsrandmemtype) {
4619: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4620: } else {
4621: if (i) *i = aij->i;
4622: if (j) *j = aij->j;
4623: if (a) *a = aij->a;
4624: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4625: }
4626: PetscFunctionReturn(PETSC_SUCCESS);
4627: }
4629: /*@C
4630: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4632: Not Collective
4634: Input Parameter:
4635: . A - a `MATSEQAIJ` matrix
4637: Output Parameter:
4638: . nz - the maximum number of nonzeros in any row
4640: Level: intermediate
4642: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4643: @*/
4644: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4645: {
4646: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4648: PetscFunctionBegin;
4649: *nz = aij->rmax;
4650: PetscFunctionReturn(PETSC_SUCCESS);
4651: }
4653: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void *data)
4654: {
4655: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data;
4657: PetscFunctionBegin;
4658: PetscCall(PetscFree(coo->perm));
4659: PetscCall(PetscFree(coo->jmap));
4660: PetscCall(PetscFree(coo));
4661: PetscFunctionReturn(PETSC_SUCCESS);
4662: }
4664: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4665: {
4666: MPI_Comm comm;
4667: PetscInt *i, *j;
4668: PetscInt M, N, row, iprev;
4669: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4670: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4671: PetscInt *Aj;
4672: PetscScalar *Aa;
4673: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)mat->data;
4674: MatType rtype;
4675: PetscCount *perm, *jmap;
4676: PetscContainer container;
4677: MatCOOStruct_SeqAIJ *coo;
4678: PetscBool isorted;
4680: PetscFunctionBegin;
4681: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4682: PetscCall(MatGetSize(mat, &M, &N));
4683: i = coo_i;
4684: j = coo_j;
4685: PetscCall(PetscMalloc1(coo_n, &perm));
4687: /* 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) */
4688: isorted = PETSC_TRUE;
4689: iprev = PETSC_INT_MIN;
4690: for (k = 0; k < coo_n; k++) {
4691: if (j[k] < 0) i[k] = -1;
4692: if (isorted) {
4693: if (i[k] < iprev) isorted = PETSC_FALSE;
4694: else iprev = i[k];
4695: }
4696: perm[k] = k;
4697: }
4699: /* Sort by row if not already */
4700: if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4702: /* Advance k to the first row with a non-negative index */
4703: for (k = 0; k < coo_n; k++)
4704: if (i[k] >= 0) break;
4705: nneg = k;
4706: 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 */
4707: nnz = 0; /* Total number of unique nonzeros to be counted */
4708: jmap++; /* Inc jmap by 1 for convenience */
4710: PetscCall(PetscCalloc1(M + 1, &Ai)); /* CSR of A */
4711: PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */
4713: /* Support for HYPRE */
4714: PetscBool hypre;
4715: const char *name;
4716: PetscCall(PetscObjectGetName((PetscObject)mat, &name));
4717: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
4719: /* In each row, sort by column, then unique column indices to get row length */
4720: Ai++; /* Inc by 1 for convenience */
4721: q = 0; /* q-th unique nonzero, with q starting from 0 */
4722: while (k < coo_n) {
4723: PetscBool strictly_sorted; // this row is strictly sorted?
4724: PetscInt jprev;
4726: /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4727: row = i[k];
4728: start = k;
4729: jprev = PETSC_INT_MIN;
4730: strictly_sorted = PETSC_TRUE;
4731: while (k < coo_n && i[k] == row) {
4732: if (strictly_sorted) {
4733: if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4734: else jprev = j[k];
4735: }
4736: k++;
4737: }
4738: end = k;
4740: /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4741: if (hypre) {
4742: PetscInt minj = PETSC_MAX_INT;
4743: PetscBool hasdiag = PETSC_FALSE;
4745: if (strictly_sorted) { // fast path to swap the first and the diag
4746: PetscCount tmp;
4747: for (p = start; p < end; p++) {
4748: if (j[p] == row && p != start) {
4749: j[p] = j[start];
4750: j[start] = row;
4751: tmp = perm[start];
4752: perm[start] = perm[p];
4753: perm[p] = tmp;
4754: break;
4755: }
4756: }
4757: } else {
4758: for (p = start; p < end; p++) {
4759: hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4760: minj = PetscMin(minj, j[p]);
4761: }
4763: if (hasdiag) {
4764: for (p = start; p < end; p++) {
4765: if (j[p] == minj) j[p] = row;
4766: else if (j[p] == row) j[p] = minj;
4767: }
4768: }
4769: }
4770: }
4771: // sort by columns in a row
4772: if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4774: if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4775: for (p = start; p < end; p++, q++) {
4776: Aj[q] = j[p];
4777: jmap[q] = 1;
4778: }
4779: Ai[row] = end - start;
4780: nnz += Ai[row]; // q is already advanced
4781: } else {
4782: /* Find number of unique col entries in this row */
4783: Aj[q] = j[start]; /* Log the first nonzero in this row */
4784: jmap[q] = 1; /* Number of repeats of this nonzero entry */
4785: Ai[row] = 1;
4786: nnz++;
4788: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4789: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4790: q++;
4791: jmap[q] = 1;
4792: Aj[q] = j[p];
4793: Ai[row]++;
4794: nnz++;
4795: } else {
4796: jmap[q]++;
4797: }
4798: }
4799: q++; /* Move to next row and thus next unique nonzero */
4800: }
4801: }
4803: Ai--; /* Back to the beginning of Ai[] */
4804: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4805: jmap--; // Back to the beginning of jmap[]
4806: jmap[0] = 0;
4807: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4809: if (nnz < coo_n - nneg) { /* Realloc with actual number of unique nonzeros */
4810: PetscCount *jmap_new;
4811: PetscInt *Aj_new;
4813: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4814: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4815: PetscCall(PetscFree(jmap));
4816: jmap = jmap_new;
4818: PetscCall(PetscMalloc1(nnz, &Aj_new));
4819: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4820: PetscCall(PetscFree(Aj));
4821: Aj = Aj_new;
4822: }
4824: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4825: PetscCount *perm_new;
4827: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4828: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4829: PetscCall(PetscFree(perm));
4830: perm = perm_new;
4831: }
4833: PetscCall(MatGetRootType_Private(mat, &rtype));
4834: PetscCall(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */
4835: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4837: seqaij->singlemalloc = PETSC_FALSE; /* Ai, Aj and Aa are not allocated in one big malloc */
4838: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4840: // Put the COO struct in a container and then attach that to the matrix
4841: PetscCall(PetscMalloc1(1, &coo));
4842: coo->nz = nnz;
4843: coo->n = coo_n;
4844: coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4845: coo->jmap = jmap; // of length nnz+1
4846: coo->perm = perm;
4847: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
4848: PetscCall(PetscContainerSetPointer(container, coo));
4849: PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_SeqAIJ));
4850: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
4851: PetscCall(PetscContainerDestroy(&container));
4852: PetscFunctionReturn(PETSC_SUCCESS);
4853: }
4855: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4856: {
4857: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4858: PetscCount i, j, Annz = aseq->nz;
4859: PetscCount *perm, *jmap;
4860: PetscScalar *Aa;
4861: PetscContainer container;
4862: MatCOOStruct_SeqAIJ *coo;
4864: PetscFunctionBegin;
4865: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4866: PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4867: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4868: perm = coo->perm;
4869: jmap = coo->jmap;
4870: PetscCall(MatSeqAIJGetArray(A, &Aa));
4871: for (i = 0; i < Annz; i++) {
4872: PetscScalar sum = 0.0;
4873: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4874: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4875: }
4876: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4877: PetscFunctionReturn(PETSC_SUCCESS);
4878: }
4880: #if defined(PETSC_HAVE_CUDA)
4881: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4882: #endif
4883: #if defined(PETSC_HAVE_HIP)
4884: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4885: #endif
4886: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4887: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4888: #endif
4890: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4891: {
4892: Mat_SeqAIJ *b;
4893: PetscMPIInt size;
4895: PetscFunctionBegin;
4896: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4897: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4899: PetscCall(PetscNew(&b));
4901: B->data = (void *)b;
4902: B->ops[0] = MatOps_Values;
4903: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4905: b->row = NULL;
4906: b->col = NULL;
4907: b->icol = NULL;
4908: b->reallocs = 0;
4909: b->ignorezeroentries = PETSC_FALSE;
4910: b->roworiented = PETSC_TRUE;
4911: b->nonew = 0;
4912: b->diag = NULL;
4913: b->solve_work = NULL;
4914: B->spptr = NULL;
4915: b->saved_values = NULL;
4916: b->idiag = NULL;
4917: b->mdiag = NULL;
4918: b->ssor_work = NULL;
4919: b->omega = 1.0;
4920: b->fshift = 0.0;
4921: b->idiagvalid = PETSC_FALSE;
4922: b->ibdiagvalid = PETSC_FALSE;
4923: b->keepnonzeropattern = PETSC_FALSE;
4925: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4926: #if defined(PETSC_HAVE_MATLAB)
4927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4929: #endif
4930: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4931: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4933: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4937: #if defined(PETSC_HAVE_MKL_SPARSE)
4938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4939: #endif
4940: #if defined(PETSC_HAVE_CUDA)
4941: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4942: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4943: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4944: #endif
4945: #if defined(PETSC_HAVE_HIP)
4946: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4949: #endif
4950: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4951: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4952: #endif
4953: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4954: #if defined(PETSC_HAVE_ELEMENTAL)
4955: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4956: #endif
4957: #if defined(PETSC_HAVE_SCALAPACK)
4958: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4959: #endif
4960: #if defined(PETSC_HAVE_HYPRE)
4961: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4962: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4963: #endif
4964: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4965: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4966: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4967: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4968: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4969: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4970: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4971: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4972: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4973: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4974: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4975: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4976: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4977: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4978: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4979: PetscCall(MatCreate_SeqAIJ_Inode(B));
4980: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4981: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4982: PetscFunctionReturn(PETSC_SUCCESS);
4983: }
4985: /*
4986: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4987: */
4988: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4989: {
4990: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4991: PetscInt m = A->rmap->n, i;
4993: PetscFunctionBegin;
4994: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4996: C->factortype = A->factortype;
4997: c->row = NULL;
4998: c->col = NULL;
4999: c->icol = NULL;
5000: c->reallocs = 0;
5001: c->diagonaldense = a->diagonaldense;
5003: C->assembled = A->assembled;
5005: if (A->preallocated) {
5006: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
5007: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
5009: if (!A->hash_active) {
5010: PetscCall(PetscMalloc1(m, &c->imax));
5011: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
5012: PetscCall(PetscMalloc1(m, &c->ilen));
5013: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
5015: /* allocate the matrix space */
5016: if (mallocmatspace) {
5017: PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i));
5019: c->singlemalloc = PETSC_TRUE;
5021: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
5022: if (m > 0) {
5023: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
5024: if (cpvalues == MAT_COPY_VALUES) {
5025: const PetscScalar *aa;
5027: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5028: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5029: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5030: } else {
5031: PetscCall(PetscArrayzero(c->a, a->i[m]));
5032: }
5033: }
5034: }
5035: C->preallocated = PETSC_TRUE;
5036: } else {
5037: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5038: PetscCall(MatSetUp(C));
5039: }
5041: c->ignorezeroentries = a->ignorezeroentries;
5042: c->roworiented = a->roworiented;
5043: c->nonew = a->nonew;
5044: if (a->diag) {
5045: PetscCall(PetscMalloc1(m + 1, &c->diag));
5046: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5047: } else c->diag = NULL;
5049: c->solve_work = NULL;
5050: c->saved_values = NULL;
5051: c->idiag = NULL;
5052: c->ssor_work = NULL;
5053: c->keepnonzeropattern = a->keepnonzeropattern;
5054: c->free_a = PETSC_TRUE;
5055: c->free_ij = PETSC_TRUE;
5057: c->rmax = a->rmax;
5058: c->nz = a->nz;
5059: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
5061: c->compressedrow.use = a->compressedrow.use;
5062: c->compressedrow.nrows = a->compressedrow.nrows;
5063: if (a->compressedrow.use) {
5064: i = a->compressedrow.nrows;
5065: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5066: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5067: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5068: } else {
5069: c->compressedrow.use = PETSC_FALSE;
5070: c->compressedrow.i = NULL;
5071: c->compressedrow.rindex = NULL;
5072: }
5073: c->nonzerorowcnt = a->nonzerorowcnt;
5074: C->nonzerostate = A->nonzerostate;
5076: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5077: }
5078: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5079: PetscFunctionReturn(PETSC_SUCCESS);
5080: }
5082: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5083: {
5084: PetscFunctionBegin;
5085: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5086: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5087: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5088: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5089: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5090: PetscFunctionReturn(PETSC_SUCCESS);
5091: }
5093: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5094: {
5095: PetscBool isbinary, ishdf5;
5097: PetscFunctionBegin;
5100: /* force binary viewer to load .info file if it has not yet done so */
5101: PetscCall(PetscViewerSetUp(viewer));
5102: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5103: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5104: if (isbinary) {
5105: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5106: } else if (ishdf5) {
5107: #if defined(PETSC_HAVE_HDF5)
5108: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5109: #else
5110: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5111: #endif
5112: } else {
5113: 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);
5114: }
5115: PetscFunctionReturn(PETSC_SUCCESS);
5116: }
5118: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5119: {
5120: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5121: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
5123: PetscFunctionBegin;
5124: PetscCall(PetscViewerSetUp(viewer));
5126: /* read in matrix header */
5127: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5128: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5129: M = header[1];
5130: N = header[2];
5131: nz = header[3];
5132: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5133: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5134: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5136: /* set block sizes from the viewer's .info file */
5137: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5138: /* set local and global sizes if not set already */
5139: if (mat->rmap->n < 0) mat->rmap->n = M;
5140: if (mat->cmap->n < 0) mat->cmap->n = N;
5141: if (mat->rmap->N < 0) mat->rmap->N = M;
5142: if (mat->cmap->N < 0) mat->cmap->N = N;
5143: PetscCall(PetscLayoutSetUp(mat->rmap));
5144: PetscCall(PetscLayoutSetUp(mat->cmap));
5146: /* check if the matrix sizes are correct */
5147: PetscCall(MatGetSize(mat, &rows, &cols));
5148: 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);
5150: /* read in row lengths */
5151: PetscCall(PetscMalloc1(M, &rowlens));
5152: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5153: /* check if sum(rowlens) is same as nz */
5154: sum = 0;
5155: for (i = 0; i < M; i++) sum += rowlens[i];
5156: 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);
5157: /* preallocate and check sizes */
5158: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5159: PetscCall(MatGetSize(mat, &rows, &cols));
5160: 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);
5161: /* store row lengths */
5162: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5163: PetscCall(PetscFree(rowlens));
5165: /* fill in "i" row pointers */
5166: a->i[0] = 0;
5167: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5168: /* read in "j" column indices */
5169: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5170: /* read in "a" nonzero values */
5171: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5173: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5174: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5175: PetscFunctionReturn(PETSC_SUCCESS);
5176: }
5178: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5179: {
5180: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5181: const PetscScalar *aa, *ba;
5182: #if defined(PETSC_USE_COMPLEX)
5183: PetscInt k;
5184: #endif
5186: PetscFunctionBegin;
5187: /* If the matrix dimensions are not equal,or no of nonzeros */
5188: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5189: *flg = PETSC_FALSE;
5190: PetscFunctionReturn(PETSC_SUCCESS);
5191: }
5193: /* if the a->i are the same */
5194: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5195: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5197: /* if a->j are the same */
5198: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5199: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5201: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5202: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5203: /* if a->a are the same */
5204: #if defined(PETSC_USE_COMPLEX)
5205: for (k = 0; k < a->nz; k++) {
5206: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5207: *flg = PETSC_FALSE;
5208: PetscFunctionReturn(PETSC_SUCCESS);
5209: }
5210: }
5211: #else
5212: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5213: #endif
5214: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5215: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5216: PetscFunctionReturn(PETSC_SUCCESS);
5217: }
5219: /*@
5220: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5221: provided by the user.
5223: Collective
5225: Input Parameters:
5226: + comm - must be an MPI communicator of size 1
5227: . m - number of rows
5228: . n - number of columns
5229: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5230: . j - column indices
5231: - a - matrix values
5233: Output Parameter:
5234: . mat - the matrix
5236: Level: intermediate
5238: Notes:
5239: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5240: once the matrix is destroyed and not before
5242: You cannot set new nonzero locations into this matrix, that will generate an error.
5244: The `i` and `j` indices are 0 based
5246: The format which is used for the sparse matrix input, is equivalent to a
5247: row-major ordering.. i.e for the following matrix, the input data expected is
5248: as shown
5249: .vb
5250: 1 0 0
5251: 2 0 3
5252: 4 5 6
5254: i = {0,1,3,6} [size = nrow+1 = 3+1]
5255: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5256: v = {1,2,3,4,5,6} [size = 6]
5257: .ve
5259: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5260: @*/
5261: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5262: {
5263: PetscInt ii;
5264: Mat_SeqAIJ *aij;
5265: PetscInt jj;
5267: PetscFunctionBegin;
5268: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5269: PetscCall(MatCreate(comm, mat));
5270: PetscCall(MatSetSizes(*mat, m, n, m, n));
5271: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5272: PetscCall(MatSetType(*mat, MATSEQAIJ));
5273: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5274: aij = (Mat_SeqAIJ *)(*mat)->data;
5275: PetscCall(PetscMalloc1(m, &aij->imax));
5276: PetscCall(PetscMalloc1(m, &aij->ilen));
5278: aij->i = i;
5279: aij->j = j;
5280: aij->a = a;
5281: aij->singlemalloc = PETSC_FALSE;
5282: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5283: aij->free_a = PETSC_FALSE;
5284: aij->free_ij = PETSC_FALSE;
5286: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5287: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5288: if (PetscDefined(USE_DEBUG)) {
5289: 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]);
5290: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5291: 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);
5292: 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);
5293: }
5294: }
5295: }
5296: if (PetscDefined(USE_DEBUG)) {
5297: for (ii = 0; ii < aij->i[m]; ii++) {
5298: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5299: 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);
5300: }
5301: }
5303: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5304: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5305: PetscFunctionReturn(PETSC_SUCCESS);
5306: }
5308: /*@
5309: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5310: provided by the user.
5312: Collective
5314: Input Parameters:
5315: + comm - must be an MPI communicator of size 1
5316: . m - number of rows
5317: . n - number of columns
5318: . i - row indices
5319: . j - column indices
5320: . a - matrix values
5321: . nz - number of nonzeros
5322: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5324: Output Parameter:
5325: . mat - the matrix
5327: Level: intermediate
5329: Example:
5330: For the following matrix, the input data expected is as shown (using 0 based indexing)
5331: .vb
5332: 1 0 0
5333: 2 0 3
5334: 4 5 6
5336: i = {0,1,1,2,2,2}
5337: j = {0,0,2,0,1,2}
5338: v = {1,2,3,4,5,6}
5339: .ve
5341: Note:
5342: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5343: and are particularly useful in iterative applications.
5345: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5346: @*/
5347: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5348: {
5349: PetscInt ii, *nnz, one = 1, row, col;
5351: PetscFunctionBegin;
5352: PetscCall(PetscCalloc1(m, &nnz));
5353: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5354: PetscCall(MatCreate(comm, mat));
5355: PetscCall(MatSetSizes(*mat, m, n, m, n));
5356: PetscCall(MatSetType(*mat, MATSEQAIJ));
5357: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5358: for (ii = 0; ii < nz; ii++) {
5359: if (idx) {
5360: row = i[ii] - 1;
5361: col = j[ii] - 1;
5362: } else {
5363: row = i[ii];
5364: col = j[ii];
5365: }
5366: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5367: }
5368: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5369: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5370: PetscCall(PetscFree(nnz));
5371: PetscFunctionReturn(PETSC_SUCCESS);
5372: }
5374: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5375: {
5376: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5378: PetscFunctionBegin;
5379: a->idiagvalid = PETSC_FALSE;
5380: a->ibdiagvalid = PETSC_FALSE;
5382: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5383: PetscFunctionReturn(PETSC_SUCCESS);
5384: }
5386: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5387: {
5388: PetscFunctionBegin;
5389: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5390: PetscFunctionReturn(PETSC_SUCCESS);
5391: }
5393: /*
5394: Permute A into C's *local* index space using rowemb,colemb.
5395: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5396: of [0,m), colemb is in [0,n).
5397: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5398: */
5399: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5400: {
5401: /* If making this function public, change the error returned in this function away from _PLIB. */
5402: Mat_SeqAIJ *Baij;
5403: PetscBool seqaij;
5404: PetscInt m, n, *nz, i, j, count;
5405: PetscScalar v;
5406: const PetscInt *rowindices, *colindices;
5408: PetscFunctionBegin;
5409: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5410: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5411: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5412: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5413: if (rowemb) {
5414: PetscCall(ISGetLocalSize(rowemb, &m));
5415: 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);
5416: } else {
5417: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5418: }
5419: if (colemb) {
5420: PetscCall(ISGetLocalSize(colemb, &n));
5421: 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);
5422: } else {
5423: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5424: }
5426: Baij = (Mat_SeqAIJ *)B->data;
5427: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5428: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5429: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5430: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5431: PetscCall(PetscFree(nz));
5432: }
5433: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5434: count = 0;
5435: rowindices = NULL;
5436: colindices = NULL;
5437: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5438: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5439: for (i = 0; i < B->rmap->n; i++) {
5440: PetscInt row;
5441: row = i;
5442: if (rowindices) row = rowindices[i];
5443: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5444: PetscInt col;
5445: col = Baij->j[count];
5446: if (colindices) col = colindices[col];
5447: v = Baij->a[count];
5448: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5449: ++count;
5450: }
5451: }
5452: /* FIXME: set C's nonzerostate correctly. */
5453: /* Assembly for C is necessary. */
5454: C->preallocated = PETSC_TRUE;
5455: C->assembled = PETSC_TRUE;
5456: C->was_assembled = PETSC_FALSE;
5457: PetscFunctionReturn(PETSC_SUCCESS);
5458: }
5460: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5461: {
5462: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5463: MatScalar *aa = a->a;
5464: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5465: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5467: PetscFunctionBegin;
5468: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5469: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5470: for (i = 1; i <= m; i++) {
5471: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5472: for (k = ai[i - 1]; k < ai[i]; k++) {
5473: if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5474: else {
5475: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5476: aa[k - fshift] = aa[k];
5477: aj[k - fshift] = aj[k];
5478: }
5479: }
5480: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5481: fshift_prev = fshift;
5482: /* reset ilen and imax for each row */
5483: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5484: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5485: rmax = PetscMax(rmax, ailen[i - 1]);
5486: }
5487: if (fshift) {
5488: if (m) {
5489: ai[m] -= fshift;
5490: a->nz = ai[m];
5491: }
5492: 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));
5493: A->nonzerostate++;
5494: A->info.nz_unneeded += (PetscReal)fshift;
5495: a->rmax = rmax;
5496: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5497: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5498: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5499: }
5500: PetscFunctionReturn(PETSC_SUCCESS);
5501: }
5503: PetscFunctionList MatSeqAIJList = NULL;
5505: /*@C
5506: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5508: Collective
5510: Input Parameters:
5511: + mat - the matrix object
5512: - matype - matrix type
5514: Options Database Key:
5515: . -mat_seqaij_type <method> - for example seqaijcrl
5517: Level: intermediate
5519: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5520: @*/
5521: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5522: {
5523: PetscBool sametype;
5524: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5526: PetscFunctionBegin;
5528: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5529: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5531: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5532: PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5533: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5534: PetscFunctionReturn(PETSC_SUCCESS);
5535: }
5537: /*@C
5538: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5540: Not Collective
5542: Input Parameters:
5543: + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5544: - function - routine to convert to subtype
5546: Level: advanced
5548: Notes:
5549: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5551: Then, your matrix can be chosen with the procedural interface at runtime via the option
5552: $ -mat_seqaij_type my_mat
5554: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5555: @*/
5556: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5557: {
5558: PetscFunctionBegin;
5559: PetscCall(MatInitializePackage());
5560: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5561: PetscFunctionReturn(PETSC_SUCCESS);
5562: }
5564: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5566: /*@C
5567: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5569: Not Collective
5571: Level: advanced
5573: Note:
5574: This registers the versions of `MATSEQAIJ` for GPUs
5576: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5577: @*/
5578: PetscErrorCode MatSeqAIJRegisterAll(void)
5579: {
5580: PetscFunctionBegin;
5581: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5582: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5584: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5585: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5586: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5587: #if defined(PETSC_HAVE_MKL_SPARSE)
5588: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5589: #endif
5590: #if defined(PETSC_HAVE_CUDA)
5591: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5592: #endif
5593: #if defined(PETSC_HAVE_HIP)
5594: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5595: #endif
5596: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5597: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5598: #endif
5599: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5600: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5601: #endif
5602: PetscFunctionReturn(PETSC_SUCCESS);
5603: }
5605: /*
5606: Special version for direct calls from Fortran
5607: */
5608: #include <petsc/private/fortranimpl.h>
5609: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5610: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5611: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5612: #define matsetvaluesseqaij_ matsetvaluesseqaij
5613: #endif
5615: /* Change these macros so can be used in void function */
5617: /* Change these macros so can be used in void function */
5618: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5619: #undef PetscCall
5620: #define PetscCall(...) \
5621: do { \
5622: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5623: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5624: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5625: return; \
5626: } \
5627: } while (0)
5629: #undef SETERRQ
5630: #define SETERRQ(comm, ierr, ...) \
5631: do { \
5632: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5633: return; \
5634: } while (0)
5636: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5637: {
5638: Mat A = *AA;
5639: PetscInt m = *mm, n = *nn;
5640: InsertMode is = *isis;
5641: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5642: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5643: PetscInt *imax, *ai, *ailen;
5644: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5645: MatScalar *ap, value, *aa;
5646: PetscBool ignorezeroentries = a->ignorezeroentries;
5647: PetscBool roworiented = a->roworiented;
5649: PetscFunctionBegin;
5650: MatCheckPreallocated(A, 1);
5651: imax = a->imax;
5652: ai = a->i;
5653: ailen = a->ilen;
5654: aj = a->j;
5655: aa = a->a;
5657: for (k = 0; k < m; k++) { /* loop over added rows */
5658: row = im[k];
5659: if (row < 0) continue;
5660: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5661: rp = aj + ai[row];
5662: ap = aa + ai[row];
5663: rmax = imax[row];
5664: nrow = ailen[row];
5665: low = 0;
5666: high = nrow;
5667: for (l = 0; l < n; l++) { /* loop over added columns */
5668: if (in[l] < 0) continue;
5669: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5670: col = in[l];
5671: if (roworiented) value = v[l + k * n];
5672: else value = v[k + l * m];
5674: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5676: if (col <= lastcol) low = 0;
5677: else high = nrow;
5678: lastcol = col;
5679: while (high - low > 5) {
5680: t = (low + high) / 2;
5681: if (rp[t] > col) high = t;
5682: else low = t;
5683: }
5684: for (i = low; i < high; i++) {
5685: if (rp[i] > col) break;
5686: if (rp[i] == col) {
5687: if (is == ADD_VALUES) ap[i] += value;
5688: else ap[i] = value;
5689: goto noinsert;
5690: }
5691: }
5692: if (value == 0.0 && ignorezeroentries) goto noinsert;
5693: if (nonew == 1) goto noinsert;
5694: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5695: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5696: N = nrow++ - 1;
5697: a->nz++;
5698: high++;
5699: /* shift up all the later entries in this row */
5700: for (ii = N; ii >= i; ii--) {
5701: rp[ii + 1] = rp[ii];
5702: ap[ii + 1] = ap[ii];
5703: }
5704: rp[i] = col;
5705: ap[i] = value;
5706: A->nonzerostate++;
5707: noinsert:;
5708: low = i + 1;
5709: }
5710: ailen[row] = nrow;
5711: }
5712: PetscFunctionReturnVoid();
5713: }
5714: /* Undefining these here since they were redefined from their original definition above! No
5715: * other PETSc functions should be defined past this point, as it is impossible to recover the
5716: * original definitions */
5717: #undef PetscCall
5718: #undef SETERRQ