Actual source code: aij.c
1: /*
2: Defines the basic matrix operations for the AIJ (compressed row)
3: matrix storage format.
4: */
6: #include <../src/mat/impls/aij/seq/aij.h>
7: #include <petscblaslapack.h>
8: #include <petscbt.h>
9: #include <petsc/private/kernels/blocktranspose.h>
11: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
12: #define TYPE AIJ
13: #define TYPE_BS
14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
15: #include "../src/mat/impls/aij/seq/seqhashmat.h"
16: #undef TYPE
17: #undef TYPE_BS
19: static PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
20: {
21: PetscBool flg;
22: char type[256];
24: PetscFunctionBegin;
25: PetscObjectOptionsBegin((PetscObject)A);
26: PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg));
27: if (flg) PetscCall(MatSeqAIJSetType(A, type));
28: PetscOptionsEnd();
29: PetscFunctionReturn(PETSC_SUCCESS);
30: }
32: static PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions)
33: {
34: PetscInt i, m, n;
35: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
37: PetscFunctionBegin;
38: PetscCall(MatGetSize(A, &m, &n));
39: PetscCall(PetscArrayzero(reductions, n));
40: if (type == NORM_2) {
41: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]);
42: } else if (type == NORM_1) {
43: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
44: } else if (type == NORM_INFINITY) {
45: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]);
46: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
47: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
48: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
49: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
50: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
52: if (type == NORM_2) {
53: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
54: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
55: for (i = 0; i < n; i++) reductions[i] /= m;
56: }
57: PetscFunctionReturn(PETSC_SUCCESS);
58: }
60: static PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is)
61: {
62: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
63: PetscInt i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs;
64: const PetscInt *jj = a->j, *ii = a->i;
65: PetscInt *rows;
67: PetscFunctionBegin;
68: for (i = 0; i < m; i++) {
69: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++;
70: }
71: PetscCall(PetscMalloc1(cnt, &rows));
72: cnt = 0;
73: for (i = 0; i < m; i++) {
74: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) {
75: rows[cnt] = i;
76: cnt++;
77: }
78: }
79: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is));
80: PetscFunctionReturn(PETSC_SUCCESS);
81: }
83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows)
84: {
85: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
86: const MatScalar *aa;
87: PetscInt i, m = A->rmap->n, cnt = 0;
88: const PetscInt *ii = a->i, *jj = a->j, *diag;
89: PetscInt *rows;
91: PetscFunctionBegin;
92: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
93: PetscCall(MatMarkDiagonal_SeqAIJ(A));
94: diag = a->diag;
95: for (i = 0; i < m; i++) {
96: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++;
97: }
98: PetscCall(PetscMalloc1(cnt, &rows));
99: cnt = 0;
100: for (i = 0; i < m; i++) {
101: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i;
102: }
103: *nrows = cnt;
104: *zrows = rows;
105: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
106: PetscFunctionReturn(PETSC_SUCCESS);
107: }
109: static PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows)
110: {
111: PetscInt nrows, *rows;
113: PetscFunctionBegin;
114: *zrows = NULL;
115: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows));
116: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows));
117: PetscFunctionReturn(PETSC_SUCCESS);
118: }
120: static PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows)
121: {
122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
123: const MatScalar *aa;
124: PetscInt m = A->rmap->n, cnt = 0;
125: const PetscInt *ii;
126: PetscInt n, i, j, *rows;
128: PetscFunctionBegin;
129: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
130: *keptrows = NULL;
131: ii = a->i;
132: for (i = 0; i < m; i++) {
133: n = ii[i + 1] - ii[i];
134: if (!n) {
135: cnt++;
136: goto ok1;
137: }
138: for (j = ii[i]; j < ii[i + 1]; j++) {
139: if (aa[j] != 0.0) goto ok1;
140: }
141: cnt++;
142: ok1:;
143: }
144: if (!cnt) {
145: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
146: PetscFunctionReturn(PETSC_SUCCESS);
147: }
148: PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows));
149: cnt = 0;
150: for (i = 0; i < m; i++) {
151: n = ii[i + 1] - ii[i];
152: if (!n) continue;
153: for (j = ii[i]; j < ii[i + 1]; j++) {
154: if (aa[j] != 0.0) {
155: rows[cnt++] = i;
156: break;
157: }
158: }
159: }
160: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
161: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows));
162: PetscFunctionReturn(PETSC_SUCCESS);
163: }
165: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is)
166: {
167: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)Y->data;
168: PetscInt i, m = Y->rmap->n;
169: const PetscInt *diag;
170: MatScalar *aa;
171: const PetscScalar *v;
172: PetscBool missing;
174: PetscFunctionBegin;
175: if (Y->assembled) {
176: PetscCall(MatMissingDiagonal_SeqAIJ(Y, &missing, NULL));
177: if (!missing) {
178: diag = aij->diag;
179: PetscCall(VecGetArrayRead(D, &v));
180: PetscCall(MatSeqAIJGetArray(Y, &aa));
181: if (is == INSERT_VALUES) {
182: for (i = 0; i < m; i++) aa[diag[i]] = v[i];
183: } else {
184: for (i = 0; i < m; i++) aa[diag[i]] += v[i];
185: }
186: PetscCall(MatSeqAIJRestoreArray(Y, &aa));
187: PetscCall(VecRestoreArrayRead(D, &v));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
190: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
191: }
192: PetscCall(MatDiagonalSet_Default(Y, D, is));
193: PetscFunctionReturn(PETSC_SUCCESS);
194: }
196: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
197: {
198: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
199: PetscInt i, ishift;
201: PetscFunctionBegin;
202: if (m) *m = A->rmap->n;
203: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
204: ishift = 0;
205: if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) {
206: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja));
207: } else if (oshift == 1) {
208: PetscInt *tia;
209: PetscInt nz = a->i[A->rmap->n];
210: /* malloc space and add 1 to i and j indices */
211: PetscCall(PetscMalloc1(A->rmap->n + 1, &tia));
212: for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1;
213: *ia = tia;
214: if (ja) {
215: PetscInt *tja;
216: PetscCall(PetscMalloc1(nz + 1, &tja));
217: for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1;
218: *ja = tja;
219: }
220: } else {
221: *ia = a->i;
222: if (ja) *ja = a->j;
223: }
224: PetscFunctionReturn(PETSC_SUCCESS);
225: }
227: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
228: {
229: PetscFunctionBegin;
230: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
231: if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) {
232: PetscCall(PetscFree(*ia));
233: if (ja) PetscCall(PetscFree(*ja));
234: }
235: PetscFunctionReturn(PETSC_SUCCESS);
236: }
238: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
239: {
240: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
241: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
242: PetscInt nz = a->i[m], row, *jj, mr, col;
244: PetscFunctionBegin;
245: *nn = n;
246: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
247: if (symmetric) {
248: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, 0, oshift, (PetscInt **)ia, (PetscInt **)ja));
249: } else {
250: PetscCall(PetscCalloc1(n, &collengths));
251: PetscCall(PetscMalloc1(n + 1, &cia));
252: PetscCall(PetscMalloc1(nz, &cja));
253: jj = a->j;
254: for (i = 0; i < nz; i++) collengths[jj[i]]++;
255: cia[0] = oshift;
256: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
257: PetscCall(PetscArrayzero(collengths, n));
258: jj = a->j;
259: for (row = 0; row < m; row++) {
260: mr = a->i[row + 1] - a->i[row];
261: for (i = 0; i < mr; i++) {
262: col = *jj++;
264: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
265: }
266: }
267: PetscCall(PetscFree(collengths));
268: *ia = cia;
269: *ja = cja;
270: }
271: PetscFunctionReturn(PETSC_SUCCESS);
272: }
274: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
275: {
276: PetscFunctionBegin;
277: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
279: PetscCall(PetscFree(*ia));
280: PetscCall(PetscFree(*ja));
281: PetscFunctionReturn(PETSC_SUCCESS);
282: }
284: /*
285: MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
286: MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
287: spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
288: */
289: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
290: {
291: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
292: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
293: PetscInt nz = a->i[m], row, mr, col, tmp;
294: PetscInt *cspidx;
295: const PetscInt *jj;
297: PetscFunctionBegin;
298: *nn = n;
299: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
301: PetscCall(PetscCalloc1(n, &collengths));
302: PetscCall(PetscMalloc1(n + 1, &cia));
303: PetscCall(PetscMalloc1(nz, &cja));
304: PetscCall(PetscMalloc1(nz, &cspidx));
305: jj = a->j;
306: for (i = 0; i < nz; i++) collengths[jj[i]]++;
307: cia[0] = oshift;
308: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
309: PetscCall(PetscArrayzero(collengths, n));
310: jj = a->j;
311: for (row = 0; row < m; row++) {
312: mr = a->i[row + 1] - a->i[row];
313: for (i = 0; i < mr; i++) {
314: col = *jj++;
315: tmp = cia[col] + collengths[col]++ - oshift;
316: cspidx[tmp] = a->i[row] + i; /* index of a->j */
317: cja[tmp] = row + oshift;
318: }
319: }
320: PetscCall(PetscFree(collengths));
321: *ia = cia;
322: *ja = cja;
323: *spidx = cspidx;
324: PetscFunctionReturn(PETSC_SUCCESS);
325: }
327: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
328: {
329: PetscFunctionBegin;
330: PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
331: PetscCall(PetscFree(*spidx));
332: PetscFunctionReturn(PETSC_SUCCESS);
333: }
335: static PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[])
336: {
337: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
338: PetscInt *ai = a->i;
339: PetscScalar *aa;
341: PetscFunctionBegin;
342: PetscCall(MatSeqAIJGetArray(A, &aa));
343: PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row]));
344: PetscCall(MatSeqAIJRestoreArray(A, &aa));
345: PetscFunctionReturn(PETSC_SUCCESS);
346: }
348: /*
349: MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions
351: - a single row of values is set with each call
352: - no row or column indices are negative or (in error) larger than the number of rows or columns
353: - the values are always added to the matrix, not set
354: - no new locations are introduced in the nonzero structure of the matrix
356: This does NOT assume the global column indices are sorted
358: */
360: #include <petsc/private/isimpl.h>
361: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
362: {
363: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
364: PetscInt low, high, t, row, nrow, i, col, l;
365: const PetscInt *rp, *ai = a->i, *ailen = a->ilen, *aj = a->j;
366: PetscInt lastcol = -1;
367: MatScalar *ap, value, *aa;
368: const PetscInt *ridx = A->rmap->mapping->indices, *cidx = A->cmap->mapping->indices;
370: PetscFunctionBegin;
371: PetscCall(MatSeqAIJGetArray(A, &aa));
372: row = ridx[im[0]];
373: rp = aj + ai[row];
374: ap = aa + ai[row];
375: nrow = ailen[row];
376: low = 0;
377: high = nrow;
378: for (l = 0; l < n; l++) { /* loop over added columns */
379: col = cidx[in[l]];
380: value = v[l];
382: if (col <= lastcol) low = 0;
383: else high = nrow;
384: lastcol = col;
385: while (high - low > 5) {
386: t = (low + high) / 2;
387: if (rp[t] > col) high = t;
388: else low = t;
389: }
390: for (i = low; i < high; i++) {
391: if (rp[i] == col) {
392: ap[i] += value;
393: low = i + 1;
394: break;
395: }
396: }
397: }
398: PetscCall(MatSeqAIJRestoreArray(A, &aa));
399: return PETSC_SUCCESS;
400: }
402: PetscErrorCode MatSetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
403: {
404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
405: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
406: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
407: PetscInt *aj = a->j, nonew = a->nonew, lastcol = -1;
408: MatScalar *ap = NULL, value = 0.0, *aa;
409: PetscBool ignorezeroentries = a->ignorezeroentries;
410: PetscBool roworiented = a->roworiented;
412: PetscFunctionBegin;
413: PetscCall(MatSeqAIJGetArray(A, &aa));
414: for (k = 0; k < m; k++) { /* loop over added rows */
415: row = im[k];
416: if (row < 0) continue;
417: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
418: rp = PetscSafePointerPlusOffset(aj, ai[row]);
419: if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, ai[row]);
420: rmax = imax[row];
421: nrow = ailen[row];
422: low = 0;
423: high = nrow;
424: for (l = 0; l < n; l++) { /* loop over added columns */
425: if (in[l] < 0) continue;
426: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
427: col = in[l];
428: if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m];
429: if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;
431: if (col <= lastcol) low = 0;
432: else high = nrow;
433: lastcol = col;
434: while (high - low > 5) {
435: t = (low + high) / 2;
436: if (rp[t] > col) high = t;
437: else low = t;
438: }
439: for (i = low; i < high; i++) {
440: if (rp[i] > col) break;
441: if (rp[i] == col) {
442: if (!A->structure_only) {
443: if (is == ADD_VALUES) {
444: ap[i] += value;
445: (void)PetscLogFlops(1.0);
446: } else ap[i] = value;
447: }
448: low = i + 1;
449: goto noinsert;
450: }
451: }
452: if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
453: if (nonew == 1) goto noinsert;
454: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") in the matrix", row, col);
455: if (A->structure_only) {
456: MatSeqXAIJReallocateAIJ_structure_only(A, A->rmap->n, 1, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
457: } else {
458: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
459: }
460: N = nrow++ - 1;
461: a->nz++;
462: high++;
463: /* shift up all the later entries in this row */
464: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
465: rp[i] = col;
466: if (!A->structure_only) {
467: PetscCall(PetscArraymove(ap + i + 1, ap + i, N - i + 1));
468: ap[i] = value;
469: }
470: low = i + 1;
471: noinsert:;
472: }
473: ailen[row] = nrow;
474: }
475: PetscCall(MatSeqAIJRestoreArray(A, &aa));
476: PetscFunctionReturn(PETSC_SUCCESS);
477: }
479: static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
480: {
481: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
482: PetscInt *rp, k, row;
483: PetscInt *ai = a->i;
484: PetscInt *aj = a->j;
485: MatScalar *aa, *ap;
487: PetscFunctionBegin;
488: PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
489: PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz);
491: PetscCall(MatSeqAIJGetArray(A, &aa));
492: for (k = 0; k < m; k++) { /* loop over added rows */
493: row = im[k];
494: rp = aj + ai[row];
495: ap = PetscSafePointerPlusOffset(aa, ai[row]);
497: PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
498: if (!A->structure_only) {
499: if (v) {
500: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
501: v += n;
502: } else {
503: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
504: }
505: }
506: a->ilen[row] = n;
507: a->imax[row] = n;
508: a->i[row + 1] = a->i[row] + n;
509: a->nz += n;
510: }
511: PetscCall(MatSeqAIJRestoreArray(A, &aa));
512: PetscFunctionReturn(PETSC_SUCCESS);
513: }
515: /*@
516: MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.
518: Input Parameters:
519: + A - the `MATSEQAIJ` matrix
520: - nztotal - bound on the number of nonzeros
522: Level: advanced
524: Notes:
525: This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
526: Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used
527: as always with multiple matrix assemblies.
529: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
530: @*/
531: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
532: {
533: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
535: PetscFunctionBegin;
536: PetscCall(PetscLayoutSetUp(A->rmap));
537: PetscCall(PetscLayoutSetUp(A->cmap));
538: a->maxnz = nztotal;
539: if (!a->imax) PetscCall(PetscMalloc1(A->rmap->n, &a->imax));
540: if (!a->ilen) {
541: PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
542: } else {
543: PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
544: }
546: /* allocate the matrix space */
547: PetscCall(PetscShmgetAllocateArray(A->rmap->n + 1, sizeof(PetscInt), (void **)&a->i));
548: PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscInt), (void **)&a->j));
549: a->free_ij = PETSC_TRUE;
550: if (A->structure_only) {
551: a->free_a = PETSC_FALSE;
552: } else {
553: PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscScalar), (void **)&a->a));
554: a->free_a = PETSC_TRUE;
555: }
556: a->i[0] = 0;
557: A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
558: A->preallocated = PETSC_TRUE;
559: PetscFunctionReturn(PETSC_SUCCESS);
560: }
562: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
563: {
564: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
565: PetscInt *rp, k, row;
566: PetscInt *ai = a->i, *ailen = a->ilen;
567: PetscInt *aj = a->j;
568: MatScalar *aa, *ap;
570: PetscFunctionBegin;
571: PetscCall(MatSeqAIJGetArray(A, &aa));
572: for (k = 0; k < m; k++) { /* loop over added rows */
573: row = im[k];
574: PetscCheck(n <= a->imax[row], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Preallocation for row %" PetscInt_FMT " does not match number of columns provided", n);
575: rp = aj + ai[row];
576: ap = aa + ai[row];
577: if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
578: if (!A->structure_only) {
579: if (v) {
580: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
581: v += n;
582: } else {
583: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
584: }
585: }
586: ailen[row] = n;
587: a->nz += n;
588: }
589: PetscCall(MatSeqAIJRestoreArray(A, &aa));
590: PetscFunctionReturn(PETSC_SUCCESS);
591: }
593: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
594: {
595: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
596: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
597: PetscInt *ai = a->i, *ailen = a->ilen;
598: const MatScalar *ap, *aa;
600: PetscFunctionBegin;
601: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
602: for (k = 0; k < m; k++) { /* loop over rows */
603: row = im[k];
604: if (row < 0) {
605: v += n;
606: continue;
607: } /* negative row */
608: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
609: rp = PetscSafePointerPlusOffset(aj, ai[row]);
610: ap = PetscSafePointerPlusOffset(aa, ai[row]);
611: nrow = ailen[row];
612: for (l = 0; l < n; l++) { /* loop over columns */
613: if (in[l] < 0) {
614: v++;
615: continue;
616: } /* negative column */
617: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
618: col = in[l];
619: high = nrow;
620: low = 0; /* assume unsorted */
621: while (high - low > 5) {
622: t = (low + high) / 2;
623: if (rp[t] > col) high = t;
624: else low = t;
625: }
626: for (i = low; i < high; i++) {
627: if (rp[i] > col) break;
628: if (rp[i] == col) {
629: *v++ = ap[i];
630: goto finished;
631: }
632: }
633: *v++ = 0.0;
634: finished:;
635: }
636: }
637: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
638: PetscFunctionReturn(PETSC_SUCCESS);
639: }
641: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
642: {
643: Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data;
644: const PetscScalar *av;
645: PetscInt header[4], M, N, m, nz, i;
646: PetscInt *rowlens;
648: PetscFunctionBegin;
649: PetscCall(PetscViewerSetUp(viewer));
651: M = mat->rmap->N;
652: N = mat->cmap->N;
653: m = mat->rmap->n;
654: nz = A->nz;
656: /* write matrix header */
657: header[0] = MAT_FILE_CLASSID;
658: header[1] = M;
659: header[2] = N;
660: header[3] = nz;
661: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
663: /* fill in and store row lengths */
664: PetscCall(PetscMalloc1(m, &rowlens));
665: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
666: if (PetscDefined(USE_DEBUG)) {
667: PetscInt mnz = 0;
669: for (i = 0; i < m; i++) mnz += rowlens[i];
670: PetscCheck(nz == mnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row lens %" PetscInt_FMT " do not sum to nz %" PetscInt_FMT, mnz, nz);
671: }
672: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
673: PetscCall(PetscFree(rowlens));
674: /* store column indices */
675: PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
676: /* store nonzero values */
677: PetscCall(MatSeqAIJGetArrayRead(mat, &av));
678: PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
679: PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));
681: /* write block size option to the viewer's .info file */
682: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
683: PetscFunctionReturn(PETSC_SUCCESS);
684: }
686: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
687: {
688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
689: PetscInt i, k, m = A->rmap->N;
691: PetscFunctionBegin;
692: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
693: for (i = 0; i < m; i++) {
694: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
695: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
696: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
697: }
698: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
699: PetscFunctionReturn(PETSC_SUCCESS);
700: }
702: static PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer)
703: {
704: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
705: const PetscScalar *av;
706: PetscInt i, j, m = A->rmap->n;
707: const char *name;
708: PetscViewerFormat format;
710: PetscFunctionBegin;
711: if (A->structure_only) {
712: PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
713: PetscFunctionReturn(PETSC_SUCCESS);
714: }
716: PetscCall(PetscViewerGetFormat(viewer, &format));
717: // By petsc's rule, even PETSC_VIEWER_ASCII_INFO_DETAIL doesn't print matrix entries
718: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);
720: /* trigger copy to CPU if needed */
721: PetscCall(MatSeqAIJGetArrayRead(A, &av));
722: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
723: if (format == PETSC_VIEWER_ASCII_MATLAB) {
724: PetscInt nofinalvalue = 0;
725: if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) {
726: /* Need a dummy value to ensure the dimension of the matrix. */
727: nofinalvalue = 1;
728: }
729: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
730: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n));
731: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz));
732: #if defined(PETSC_USE_COMPLEX)
733: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue));
734: #else
735: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue));
736: #endif
737: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n"));
739: for (i = 0; i < m; i++) {
740: for (j = a->i[i]; j < a->i[i + 1]; j++) {
741: #if defined(PETSC_USE_COMPLEX)
742: 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])));
743: #else
744: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j]));
745: #endif
746: }
747: }
748: if (nofinalvalue) {
749: #if defined(PETSC_USE_COMPLEX)
750: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.));
751: #else
752: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0));
753: #endif
754: }
755: PetscCall(PetscObjectGetName((PetscObject)A, &name));
756: PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name));
757: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
758: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
759: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
760: for (i = 0; i < m; i++) {
761: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
762: for (j = a->i[i]; j < a->i[i + 1]; j++) {
763: #if defined(PETSC_USE_COMPLEX)
764: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
765: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
766: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
767: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
768: } else if (PetscRealPart(a->a[j]) != 0.0) {
769: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
770: }
771: #else
772: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
773: #endif
774: }
775: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
776: }
777: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
778: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
779: PetscInt nzd = 0, fshift = 1, *sptr;
780: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
781: PetscCall(PetscMalloc1(m + 1, &sptr));
782: for (i = 0; i < m; i++) {
783: sptr[i] = nzd + 1;
784: for (j = a->i[i]; j < a->i[i + 1]; j++) {
785: if (a->j[j] >= i) {
786: #if defined(PETSC_USE_COMPLEX)
787: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
788: #else
789: if (a->a[j] != 0.0) nzd++;
790: #endif
791: }
792: }
793: }
794: sptr[m] = nzd + 1;
795: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd));
796: for (i = 0; i < m + 1; i += 6) {
797: if (i + 4 < m) {
798: 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]));
799: } else if (i + 3 < m) {
800: 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]));
801: } else if (i + 2 < m) {
802: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3]));
803: } else if (i + 1 < m) {
804: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2]));
805: } else if (i < m) {
806: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1]));
807: } else {
808: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i]));
809: }
810: }
811: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
812: PetscCall(PetscFree(sptr));
813: for (i = 0; i < m; i++) {
814: for (j = a->i[i]; j < a->i[i + 1]; j++) {
815: if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift));
816: }
817: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
818: }
819: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
820: for (i = 0; i < m; i++) {
821: for (j = a->i[i]; j < a->i[i + 1]; j++) {
822: if (a->j[j] >= i) {
823: #if defined(PETSC_USE_COMPLEX)
824: 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])));
825: #else
826: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j]));
827: #endif
828: }
829: }
830: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
831: }
832: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
833: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
834: PetscInt cnt = 0, jcnt;
835: PetscScalar value;
836: #if defined(PETSC_USE_COMPLEX)
837: PetscBool realonly = PETSC_TRUE;
839: for (i = 0; i < a->i[m]; i++) {
840: if (PetscImaginaryPart(a->a[i]) != 0.0) {
841: realonly = PETSC_FALSE;
842: break;
843: }
844: }
845: #endif
847: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
848: for (i = 0; i < m; i++) {
849: jcnt = 0;
850: for (j = 0; j < A->cmap->n; j++) {
851: if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) {
852: value = a->a[cnt++];
853: jcnt++;
854: } else {
855: value = 0.0;
856: }
857: #if defined(PETSC_USE_COMPLEX)
858: if (realonly) {
859: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value)));
860: } else {
861: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value)));
862: }
863: #else
864: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value));
865: #endif
866: }
867: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
868: }
869: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
870: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
871: PetscInt fshift = 1;
872: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
873: #if defined(PETSC_USE_COMPLEX)
874: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n"));
875: #else
876: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n"));
877: #endif
878: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz));
879: for (i = 0; i < m; i++) {
880: for (j = a->i[i]; j < a->i[i + 1]; j++) {
881: #if defined(PETSC_USE_COMPLEX)
882: 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])));
883: #else
884: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j]));
885: #endif
886: }
887: }
888: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
889: } else {
890: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
891: if (A->factortype) {
892: for (i = 0; i < m; i++) {
893: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
894: /* L part */
895: for (j = a->i[i]; j < a->i[i + 1]; j++) {
896: #if defined(PETSC_USE_COMPLEX)
897: if (PetscImaginaryPart(a->a[j]) > 0.0) {
898: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
899: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
900: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
901: } else {
902: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
903: }
904: #else
905: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
906: #endif
907: }
908: /* diagonal */
909: j = a->diag[i];
910: #if defined(PETSC_USE_COMPLEX)
911: if (PetscImaginaryPart(a->a[j]) > 0.0) {
912: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)PetscImaginaryPart(1 / a->a[j])));
913: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
914: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)(-PetscImaginaryPart(1 / a->a[j]))));
915: } else {
916: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1 / a->a[j])));
917: }
918: #else
919: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1 / a->a[j])));
920: #endif
922: /* U part */
923: for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) {
924: #if defined(PETSC_USE_COMPLEX)
925: if (PetscImaginaryPart(a->a[j]) > 0.0) {
926: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
927: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
928: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
929: } else {
930: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
931: }
932: #else
933: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
934: #endif
935: }
936: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
937: }
938: } else {
939: for (i = 0; i < m; i++) {
940: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
941: for (j = a->i[i]; j < a->i[i + 1]; j++) {
942: #if defined(PETSC_USE_COMPLEX)
943: if (PetscImaginaryPart(a->a[j]) > 0.0) {
944: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
945: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
946: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
947: } else {
948: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
949: }
950: #else
951: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
952: #endif
953: }
954: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
955: }
956: }
957: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
958: }
959: PetscCall(PetscViewerFlush(viewer));
960: PetscFunctionReturn(PETSC_SUCCESS);
961: }
963: #include <petscdraw.h>
964: static PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
965: {
966: Mat A = (Mat)Aa;
967: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
968: PetscInt i, j, m = A->rmap->n;
969: int color;
970: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
971: PetscViewer viewer;
972: PetscViewerFormat format;
973: const PetscScalar *aa;
975: PetscFunctionBegin;
976: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
977: PetscCall(PetscViewerGetFormat(viewer, &format));
978: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
980: /* loop over matrix elements drawing boxes */
981: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
982: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
983: PetscDrawCollectiveBegin(draw);
984: /* Blue for negative, Cyan for zero and Red for positive */
985: color = PETSC_DRAW_BLUE;
986: for (i = 0; i < m; i++) {
987: y_l = m - i - 1.0;
988: y_r = y_l + 1.0;
989: for (j = a->i[i]; j < a->i[i + 1]; j++) {
990: x_l = a->j[j];
991: x_r = x_l + 1.0;
992: if (PetscRealPart(aa[j]) >= 0.) continue;
993: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
994: }
995: }
996: color = PETSC_DRAW_CYAN;
997: for (i = 0; i < m; i++) {
998: y_l = m - i - 1.0;
999: y_r = y_l + 1.0;
1000: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1001: x_l = a->j[j];
1002: x_r = x_l + 1.0;
1003: if (aa[j] != 0.) continue;
1004: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1005: }
1006: }
1007: color = PETSC_DRAW_RED;
1008: for (i = 0; i < m; i++) {
1009: y_l = m - i - 1.0;
1010: y_r = y_l + 1.0;
1011: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1012: x_l = a->j[j];
1013: x_r = x_l + 1.0;
1014: if (PetscRealPart(aa[j]) <= 0.) continue;
1015: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1016: }
1017: }
1018: PetscDrawCollectiveEnd(draw);
1019: } else {
1020: /* use contour shading to indicate magnitude of values */
1021: /* first determine max of all nonzero values */
1022: PetscReal minv = 0.0, maxv = 0.0;
1023: PetscInt nz = a->nz, count = 0;
1024: PetscDraw popup;
1026: for (i = 0; i < nz; i++) {
1027: if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1028: }
1029: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1030: PetscCall(PetscDrawGetPopup(draw, &popup));
1031: PetscCall(PetscDrawScalePopup(popup, minv, maxv));
1033: PetscDrawCollectiveBegin(draw);
1034: for (i = 0; i < m; i++) {
1035: y_l = m - i - 1.0;
1036: y_r = y_l + 1.0;
1037: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1038: x_l = a->j[j];
1039: x_r = x_l + 1.0;
1040: color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1041: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1042: count++;
1043: }
1044: }
1045: PetscDrawCollectiveEnd(draw);
1046: }
1047: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1048: PetscFunctionReturn(PETSC_SUCCESS);
1049: }
1051: #include <petscdraw.h>
1052: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1053: {
1054: PetscDraw draw;
1055: PetscReal xr, yr, xl, yl, h, w;
1056: PetscBool isnull;
1058: PetscFunctionBegin;
1059: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1060: PetscCall(PetscDrawIsNull(draw, &isnull));
1061: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1063: xr = A->cmap->n;
1064: yr = A->rmap->n;
1065: h = yr / 10.0;
1066: w = xr / 10.0;
1067: xr += w;
1068: yr += h;
1069: xl = -w;
1070: yl = -h;
1071: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1072: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1073: PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1074: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1075: PetscCall(PetscDrawSave(draw));
1076: PetscFunctionReturn(PETSC_SUCCESS);
1077: }
1079: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1080: {
1081: PetscBool isascii, isbinary, isdraw;
1083: PetscFunctionBegin;
1084: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1085: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1086: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1087: if (isascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1088: else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1089: else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1090: PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1091: PetscFunctionReturn(PETSC_SUCCESS);
1092: }
1094: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1095: {
1096: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1097: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1098: PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n;
1099: MatScalar *aa = a->a, *ap;
1100: PetscReal ratio = 0.6;
1102: PetscFunctionBegin;
1103: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
1104: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1105: if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1106: /* we need to respect users asking to use or not the inodes routine in between matrix assemblies, e.g., via MatSetOption(A, MAT_USE_INODES, val) */
1107: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode)); /* read the sparsity pattern */
1108: PetscFunctionReturn(PETSC_SUCCESS);
1109: }
1111: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1112: for (i = 1; i < m; i++) {
1113: /* move each row back by the amount of empty slots (fshift) before it*/
1114: fshift += imax[i - 1] - ailen[i - 1];
1115: rmax = PetscMax(rmax, ailen[i]);
1116: if (fshift) {
1117: ip = aj + ai[i];
1118: ap = aa + ai[i];
1119: N = ailen[i];
1120: PetscCall(PetscArraymove(ip - fshift, ip, N));
1121: if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1122: }
1123: ai[i] = ai[i - 1] + ailen[i - 1];
1124: }
1125: if (m) {
1126: fshift += imax[m - 1] - ailen[m - 1];
1127: ai[m] = ai[m - 1] + ailen[m - 1];
1128: }
1129: /* reset ilen and imax for each row */
1130: a->nonzerorowcnt = 0;
1131: if (A->structure_only) {
1132: PetscCall(PetscFree(a->imax));
1133: PetscCall(PetscFree(a->ilen));
1134: } else { /* !A->structure_only */
1135: for (i = 0; i < m; i++) {
1136: ailen[i] = imax[i] = ai[i + 1] - ai[i];
1137: a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1138: }
1139: }
1140: a->nz = ai[m];
1141: 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);
1142: PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
1143: a->diagonaldense = PETSC_TRUE;
1144: n = PetscMin(A->rmap->n, A->cmap->n);
1145: for (i = 0; i < n; i++) {
1146: if (a->diag[i] >= ai[i + 1]) {
1147: a->diagonaldense = PETSC_FALSE;
1148: break;
1149: }
1150: }
1151: 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));
1152: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1153: PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));
1155: A->info.mallocs += a->reallocs;
1156: a->reallocs = 0;
1157: A->info.nz_unneeded = (PetscReal)fshift;
1158: a->rmax = rmax;
1160: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1161: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1162: PetscFunctionReturn(PETSC_SUCCESS);
1163: }
1165: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1166: {
1167: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1168: PetscInt i, nz = a->nz;
1169: MatScalar *aa;
1171: PetscFunctionBegin;
1172: PetscCall(MatSeqAIJGetArray(A, &aa));
1173: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1174: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1175: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1176: PetscFunctionReturn(PETSC_SUCCESS);
1177: }
1179: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1180: {
1181: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1182: PetscInt i, nz = a->nz;
1183: MatScalar *aa;
1185: PetscFunctionBegin;
1186: PetscCall(MatSeqAIJGetArray(A, &aa));
1187: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1188: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1189: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1190: PetscFunctionReturn(PETSC_SUCCESS);
1191: }
1193: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1194: {
1195: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1196: MatScalar *aa;
1198: PetscFunctionBegin;
1199: PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1200: PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1201: PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1202: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1203: PetscFunctionReturn(PETSC_SUCCESS);
1204: }
1206: static PetscErrorCode MatReset_SeqAIJ(Mat A)
1207: {
1208: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1210: PetscFunctionBegin;
1211: if (A->hash_active) {
1212: A->ops[0] = a->cops;
1213: PetscCall(PetscHMapIJVDestroy(&a->ht));
1214: PetscCall(PetscFree(a->dnz));
1215: A->hash_active = PETSC_FALSE;
1216: }
1218: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1219: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1220: PetscCall(ISDestroy(&a->row));
1221: PetscCall(ISDestroy(&a->col));
1222: PetscCall(PetscFree(a->diag));
1223: PetscCall(PetscFree(a->ibdiag));
1224: PetscCall(PetscFree(a->imax));
1225: PetscCall(PetscFree(a->ilen));
1226: PetscCall(PetscFree(a->ipre));
1227: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1228: PetscCall(PetscFree(a->solve_work));
1229: PetscCall(ISDestroy(&a->icol));
1230: PetscCall(PetscFree(a->saved_values));
1231: a->compressedrow.use = PETSC_FALSE;
1232: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1233: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1234: PetscFunctionReturn(PETSC_SUCCESS);
1235: }
1237: static PetscErrorCode MatResetHash_SeqAIJ(Mat A)
1238: {
1239: PetscFunctionBegin;
1240: PetscCall(MatReset_SeqAIJ(A));
1241: PetscCall(MatCreate_SeqAIJ_Inode(A));
1242: PetscCall(MatSetUp_Seq_Hash(A));
1243: A->nonzerostate++;
1244: PetscFunctionReturn(PETSC_SUCCESS);
1245: }
1247: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1248: {
1249: PetscFunctionBegin;
1250: PetscCall(MatReset_SeqAIJ(A));
1251: PetscCall(PetscFree(A->data));
1253: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1254: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1255: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1256: users reusing the matrix object with different data to incur in obscure segmentation faults
1257: due to different matrix sizes */
1258: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1260: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1261: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1262: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1263: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1265: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1266: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1267: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1268: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1270: #if defined(PETSC_HAVE_MKL_SPARSE)
1271: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1272: #endif
1273: #if defined(PETSC_HAVE_CUDA)
1274: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1275: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1276: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1277: #endif
1278: #if defined(PETSC_HAVE_HIP)
1279: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1280: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1282: #endif
1283: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1284: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1285: #endif
1286: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1287: #if defined(PETSC_HAVE_ELEMENTAL)
1288: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1289: #endif
1290: #if defined(PETSC_HAVE_SCALAPACK)
1291: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1292: #endif
1293: #if defined(PETSC_HAVE_HYPRE)
1294: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1296: #endif
1297: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1302: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1303: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1304: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetHash_C", NULL));
1305: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1306: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1307: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1308: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1309: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1310: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1311: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1312: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1313: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1314: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1315: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1316: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1317: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1318: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1319: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1320: PetscFunctionReturn(PETSC_SUCCESS);
1321: }
1323: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1324: {
1325: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1327: PetscFunctionBegin;
1328: switch (op) {
1329: case MAT_ROW_ORIENTED:
1330: a->roworiented = flg;
1331: break;
1332: case MAT_KEEP_NONZERO_PATTERN:
1333: a->keepnonzeropattern = flg;
1334: break;
1335: case MAT_NEW_NONZERO_LOCATIONS:
1336: a->nonew = (flg ? 0 : 1);
1337: break;
1338: case MAT_NEW_NONZERO_LOCATION_ERR:
1339: a->nonew = (flg ? -1 : 0);
1340: break;
1341: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1342: a->nonew = (flg ? -2 : 0);
1343: break;
1344: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1345: a->nounused = (flg ? -1 : 0);
1346: break;
1347: case MAT_IGNORE_ZERO_ENTRIES:
1348: a->ignorezeroentries = flg;
1349: break;
1350: case MAT_USE_INODES:
1351: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1352: break;
1353: case MAT_SUBMAT_SINGLEIS:
1354: A->submat_singleis = flg;
1355: break;
1356: case MAT_SORTED_FULL:
1357: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1358: else A->ops->setvalues = MatSetValues_SeqAIJ;
1359: break;
1360: case MAT_FORM_EXPLICIT_TRANSPOSE:
1361: A->form_explicit_transpose = flg;
1362: break;
1363: default:
1364: break;
1365: }
1366: PetscFunctionReturn(PETSC_SUCCESS);
1367: }
1369: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1370: {
1371: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1372: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1373: PetscScalar *x;
1374: const PetscScalar *aa;
1376: PetscFunctionBegin;
1377: PetscCall(VecGetLocalSize(v, &n));
1378: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1379: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1380: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1381: PetscInt *diag = a->diag;
1382: PetscCall(VecGetArrayWrite(v, &x));
1383: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1384: PetscCall(VecRestoreArrayWrite(v, &x));
1385: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1386: PetscFunctionReturn(PETSC_SUCCESS);
1387: }
1389: PetscCall(VecGetArrayWrite(v, &x));
1390: for (i = 0; i < n; i++) {
1391: x[i] = 0.0;
1392: for (j = ai[i]; j < ai[i + 1]; j++) {
1393: if (aj[j] == i) {
1394: x[i] = aa[j];
1395: break;
1396: }
1397: }
1398: }
1399: PetscCall(VecRestoreArrayWrite(v, &x));
1400: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1401: PetscFunctionReturn(PETSC_SUCCESS);
1402: }
1404: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1405: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1406: {
1407: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1408: const MatScalar *aa;
1409: PetscScalar *y;
1410: const PetscScalar *x;
1411: PetscInt m = A->rmap->n;
1412: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1413: const MatScalar *v;
1414: PetscScalar alpha;
1415: PetscInt n, i, j;
1416: const PetscInt *idx, *ii, *ridx = NULL;
1417: Mat_CompressedRow cprow = a->compressedrow;
1418: PetscBool usecprow = cprow.use;
1419: #endif
1421: PetscFunctionBegin;
1422: if (zz != yy) PetscCall(VecCopy(zz, yy));
1423: PetscCall(VecGetArrayRead(xx, &x));
1424: PetscCall(VecGetArray(yy, &y));
1425: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1427: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1428: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1429: #else
1430: if (usecprow) {
1431: m = cprow.nrows;
1432: ii = cprow.i;
1433: ridx = cprow.rindex;
1434: } else {
1435: ii = a->i;
1436: }
1437: for (i = 0; i < m; i++) {
1438: idx = a->j + ii[i];
1439: v = aa + ii[i];
1440: n = ii[i + 1] - ii[i];
1441: if (usecprow) {
1442: alpha = x[ridx[i]];
1443: } else {
1444: alpha = x[i];
1445: }
1446: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1447: }
1448: #endif
1449: PetscCall(PetscLogFlops(2.0 * a->nz));
1450: PetscCall(VecRestoreArrayRead(xx, &x));
1451: PetscCall(VecRestoreArray(yy, &y));
1452: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1453: PetscFunctionReturn(PETSC_SUCCESS);
1454: }
1456: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1457: {
1458: PetscFunctionBegin;
1459: PetscCall(VecSet(yy, 0.0));
1460: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1461: PetscFunctionReturn(PETSC_SUCCESS);
1462: }
1464: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1466: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1467: {
1468: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1469: PetscScalar *y;
1470: const PetscScalar *x;
1471: const MatScalar *a_a;
1472: PetscInt m = A->rmap->n;
1473: const PetscInt *ii, *ridx = NULL;
1474: PetscBool usecprow = a->compressedrow.use;
1476: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1477: #pragma disjoint(*x, *y, *aa)
1478: #endif
1480: PetscFunctionBegin;
1481: if (a->inode.use && a->inode.checked) {
1482: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1483: PetscFunctionReturn(PETSC_SUCCESS);
1484: }
1485: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1486: PetscCall(VecGetArrayRead(xx, &x));
1487: PetscCall(VecGetArray(yy, &y));
1488: ii = a->i;
1489: if (usecprow) { /* use compressed row format */
1490: PetscCall(PetscArrayzero(y, m));
1491: m = a->compressedrow.nrows;
1492: ii = a->compressedrow.i;
1493: ridx = a->compressedrow.rindex;
1494: PetscPragmaUseOMPKernels(parallel for)
1495: for (PetscInt i = 0; i < m; i++) {
1496: PetscInt n = ii[i + 1] - ii[i];
1497: const PetscInt *aj = a->j + ii[i];
1498: const PetscScalar *aa = a_a + ii[i];
1499: PetscScalar sum = 0.0;
1500: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1501: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1502: y[ridx[i]] = sum;
1503: }
1504: } else { /* do not use compressed row format */
1505: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1506: fortranmultaij_(&m, x, ii, a->j, a_a, y);
1507: #else
1508: PetscPragmaUseOMPKernels(parallel for)
1509: for (PetscInt i = 0; i < m; i++) {
1510: PetscInt n = ii[i + 1] - ii[i];
1511: const PetscInt *aj = a->j + ii[i];
1512: const PetscScalar *aa = a_a + ii[i];
1513: PetscScalar sum = 0.0;
1514: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1515: y[i] = sum;
1516: }
1517: #endif
1518: }
1519: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1520: PetscCall(VecRestoreArrayRead(xx, &x));
1521: PetscCall(VecRestoreArray(yy, &y));
1522: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1523: PetscFunctionReturn(PETSC_SUCCESS);
1524: }
1526: // HACK!!!!! Used by src/mat/tests/ex170.c
1527: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1528: {
1529: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1530: PetscScalar *y;
1531: const PetscScalar *x;
1532: const MatScalar *aa, *a_a;
1533: PetscInt m = A->rmap->n;
1534: const PetscInt *aj, *ii, *ridx = NULL;
1535: PetscInt n, i, nonzerorow = 0;
1536: PetscScalar sum;
1537: PetscBool usecprow = a->compressedrow.use;
1539: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1540: #pragma disjoint(*x, *y, *aa)
1541: #endif
1543: PetscFunctionBegin;
1544: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1545: PetscCall(VecGetArrayRead(xx, &x));
1546: PetscCall(VecGetArray(yy, &y));
1547: if (usecprow) { /* use compressed row format */
1548: m = a->compressedrow.nrows;
1549: ii = a->compressedrow.i;
1550: ridx = a->compressedrow.rindex;
1551: for (i = 0; i < m; i++) {
1552: n = ii[i + 1] - ii[i];
1553: aj = a->j + ii[i];
1554: aa = a_a + ii[i];
1555: sum = 0.0;
1556: nonzerorow += (n > 0);
1557: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1558: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1559: y[*ridx++] = sum;
1560: }
1561: } else { /* do not use compressed row format */
1562: ii = a->i;
1563: for (i = 0; i < m; i++) {
1564: n = ii[i + 1] - ii[i];
1565: aj = a->j + ii[i];
1566: aa = a_a + ii[i];
1567: sum = 0.0;
1568: nonzerorow += (n > 0);
1569: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1570: y[i] = sum;
1571: }
1572: }
1573: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1574: PetscCall(VecRestoreArrayRead(xx, &x));
1575: PetscCall(VecRestoreArray(yy, &y));
1576: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1577: PetscFunctionReturn(PETSC_SUCCESS);
1578: }
1580: // HACK!!!!! Used by src/mat/tests/ex170.c
1581: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1582: {
1583: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1584: PetscScalar *y, *z;
1585: const PetscScalar *x;
1586: const MatScalar *aa, *a_a;
1587: PetscInt m = A->rmap->n, *aj, *ii;
1588: PetscInt n, i, *ridx = NULL;
1589: PetscScalar sum;
1590: PetscBool usecprow = a->compressedrow.use;
1592: PetscFunctionBegin;
1593: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1594: PetscCall(VecGetArrayRead(xx, &x));
1595: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1596: if (usecprow) { /* use compressed row format */
1597: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1598: m = a->compressedrow.nrows;
1599: ii = a->compressedrow.i;
1600: ridx = a->compressedrow.rindex;
1601: for (i = 0; i < m; i++) {
1602: n = ii[i + 1] - ii[i];
1603: aj = a->j + ii[i];
1604: aa = a_a + ii[i];
1605: sum = y[*ridx];
1606: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1607: z[*ridx++] = sum;
1608: }
1609: } else { /* do not use compressed row format */
1610: ii = a->i;
1611: for (i = 0; i < m; i++) {
1612: n = ii[i + 1] - ii[i];
1613: aj = a->j + ii[i];
1614: aa = a_a + ii[i];
1615: sum = y[i];
1616: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1617: z[i] = sum;
1618: }
1619: }
1620: PetscCall(PetscLogFlops(2.0 * a->nz));
1621: PetscCall(VecRestoreArrayRead(xx, &x));
1622: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1623: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1624: PetscFunctionReturn(PETSC_SUCCESS);
1625: }
1627: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1628: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1629: {
1630: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1631: PetscScalar *y, *z;
1632: const PetscScalar *x;
1633: const MatScalar *a_a;
1634: const PetscInt *ii, *ridx = NULL;
1635: PetscInt m = A->rmap->n;
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 (PetscInt i = 0; i < m; i++) {
1652: PetscInt n = ii[i + 1] - ii[i];
1653: const PetscInt *aj = a->j + ii[i];
1654: const PetscScalar *aa = a_a + ii[i];
1655: PetscScalar 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: fortranmultaddaij_(&m, x, ii, a->j, a_a, y, z);
1663: #else
1664: PetscPragmaUseOMPKernels(parallel for)
1665: for (PetscInt i = 0; i < m; i++) {
1666: PetscInt n = ii[i + 1] - ii[i];
1667: const PetscInt *aj = a->j + ii[i];
1668: const PetscScalar *aa = a_a + ii[i];
1669: PetscScalar sum = y[i];
1670: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1671: z[i] = sum;
1672: }
1673: #endif
1674: }
1675: PetscCall(PetscLogFlops(2.0 * a->nz));
1676: PetscCall(VecRestoreArrayRead(xx, &x));
1677: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1678: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1679: PetscFunctionReturn(PETSC_SUCCESS);
1680: }
1682: /*
1683: Adds diagonal pointers to sparse matrix nonzero structure.
1684: */
1685: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1686: {
1687: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1688: PetscInt i, j, m = A->rmap->n;
1689: PetscBool alreadySet = PETSC_TRUE;
1691: PetscFunctionBegin;
1692: if (!a->diag) {
1693: PetscCall(PetscMalloc1(m, &a->diag));
1694: alreadySet = PETSC_FALSE;
1695: }
1696: for (i = 0; i < A->rmap->n; i++) {
1697: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1698: if (alreadySet) {
1699: PetscInt pos = a->diag[i];
1700: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1701: }
1703: a->diag[i] = a->i[i + 1];
1704: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1705: if (a->j[j] == i) {
1706: a->diag[i] = j;
1707: break;
1708: }
1709: }
1710: }
1711: PetscFunctionReturn(PETSC_SUCCESS);
1712: }
1714: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1715: {
1716: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1717: const PetscInt *diag = (const PetscInt *)a->diag;
1718: const PetscInt *ii = (const PetscInt *)a->i;
1719: PetscInt i, *mdiag = NULL;
1720: PetscInt cnt = 0; /* how many diagonals are missing */
1722: PetscFunctionBegin;
1723: if (!A->preallocated || !a->nz) {
1724: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1725: PetscCall(MatShift_Basic(A, v));
1726: PetscFunctionReturn(PETSC_SUCCESS);
1727: }
1729: if (a->diagonaldense) {
1730: cnt = 0;
1731: } else {
1732: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1733: for (i = 0; i < A->rmap->n; i++) {
1734: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1735: cnt++;
1736: mdiag[i] = 1;
1737: }
1738: }
1739: }
1740: if (!cnt) {
1741: PetscCall(MatShift_Basic(A, v));
1742: } else {
1743: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1744: PetscInt *oldj = a->j, *oldi = a->i;
1745: PetscBool free_a = a->free_a, free_ij = a->free_ij;
1746: const PetscScalar *Aa;
1748: PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1749: PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));
1751: a->a = NULL;
1752: a->j = NULL;
1753: a->i = NULL;
1754: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1755: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1756: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1758: /* copy old values into new matrix data structure */
1759: for (i = 0; i < A->rmap->n; i++) {
1760: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1761: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1762: }
1763: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1764: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1765: if (free_a) PetscCall(PetscShmgetDeallocateArray((void **)&olda));
1766: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldj));
1767: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldi));
1768: }
1769: PetscCall(PetscFree(mdiag));
1770: a->diagonaldense = PETSC_TRUE;
1771: PetscFunctionReturn(PETSC_SUCCESS);
1772: }
1774: /*
1775: Checks for missing diagonals
1776: */
1777: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1778: {
1779: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1780: PetscInt *diag, *ii = a->i, i;
1782: PetscFunctionBegin;
1783: *missing = PETSC_FALSE;
1784: if (A->rmap->n > 0 && !ii) {
1785: *missing = PETSC_TRUE;
1786: if (d) *d = 0;
1787: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1788: } else {
1789: PetscInt n;
1790: n = PetscMin(A->rmap->n, A->cmap->n);
1791: diag = a->diag;
1792: for (i = 0; i < n; i++) {
1793: if (diag[i] >= ii[i + 1]) {
1794: *missing = PETSC_TRUE;
1795: if (d) *d = i;
1796: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1797: break;
1798: }
1799: }
1800: }
1801: PetscFunctionReturn(PETSC_SUCCESS);
1802: }
1804: #include <petscblaslapack.h>
1805: #include <petsc/private/kernels/blockinvert.h>
1807: /*
1808: Note that values is allocated externally by the PC and then passed into this routine
1809: */
1810: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1811: {
1812: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1813: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1814: const PetscReal shift = 0.0;
1815: PetscInt ipvt[5];
1816: PetscCount flops = 0;
1817: PetscScalar work[25], *v_work;
1819: PetscFunctionBegin;
1820: allowzeropivot = PetscNot(A->erroriffailure);
1821: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1822: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1823: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1824: PetscCall(PetscMalloc1(bsizemax, &indx));
1825: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1826: ncnt = 0;
1827: for (i = 0; i < nblocks; i++) {
1828: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1829: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1830: switch (bsizes[i]) {
1831: case 1:
1832: *diag = 1.0 / (*diag);
1833: break;
1834: case 2:
1835: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1836: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1837: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1838: break;
1839: case 3:
1840: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1841: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1842: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1843: break;
1844: case 4:
1845: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1846: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1847: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1848: break;
1849: case 5:
1850: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1851: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1852: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1853: break;
1854: case 6:
1855: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1856: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1857: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1858: break;
1859: case 7:
1860: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1861: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1862: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1863: break;
1864: default:
1865: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1866: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1867: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1868: }
1869: ncnt += bsizes[i];
1870: diag += bsizes[i] * bsizes[i];
1871: flops += 2 * PetscPowInt64(bsizes[i], 3) / 3;
1872: }
1873: PetscCall(PetscLogFlops(flops));
1874: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1875: PetscCall(PetscFree(indx));
1876: PetscFunctionReturn(PETSC_SUCCESS);
1877: }
1879: /*
1880: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1881: */
1882: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1883: {
1884: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1885: PetscInt i, *diag, m = A->rmap->n;
1886: const MatScalar *v;
1887: PetscScalar *idiag, *mdiag;
1889: PetscFunctionBegin;
1890: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1891: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1892: diag = a->diag;
1893: if (!a->idiag) PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work));
1895: mdiag = a->mdiag;
1896: idiag = a->idiag;
1897: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1898: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1899: for (i = 0; i < m; i++) {
1900: mdiag[i] = v[diag[i]];
1901: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1902: PetscCheck(PetscRealPart(fshift), PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1903: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1904: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1905: A->factorerror_zeropivot_value = 0.0;
1906: A->factorerror_zeropivot_row = i;
1907: }
1908: idiag[i] = 1.0 / v[diag[i]];
1909: }
1910: PetscCall(PetscLogFlops(m));
1911: } else {
1912: for (i = 0; i < m; i++) {
1913: mdiag[i] = v[diag[i]];
1914: idiag[i] = omega / (fshift + v[diag[i]]);
1915: }
1916: PetscCall(PetscLogFlops(2.0 * m));
1917: }
1918: a->idiagvalid = PETSC_TRUE;
1919: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1920: PetscFunctionReturn(PETSC_SUCCESS);
1921: }
1923: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1924: {
1925: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1926: PetscScalar *x, d, sum, *t, scale;
1927: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1928: const PetscScalar *b, *bs, *xb, *ts;
1929: PetscInt n, m = A->rmap->n, i;
1930: const PetscInt *idx, *diag;
1932: PetscFunctionBegin;
1933: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1934: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1935: PetscFunctionReturn(PETSC_SUCCESS);
1936: }
1937: its = its * lits;
1939: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1940: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1941: a->fshift = fshift;
1942: a->omega = omega;
1944: diag = a->diag;
1945: t = a->ssor_work;
1946: idiag = a->idiag;
1947: mdiag = a->mdiag;
1949: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1950: PetscCall(VecGetArray(xx, &x));
1951: PetscCall(VecGetArrayRead(bb, &b));
1952: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1953: if (flag == SOR_APPLY_UPPER) {
1954: /* apply (U + D/omega) to the vector */
1955: bs = b;
1956: for (i = 0; i < m; i++) {
1957: d = fshift + mdiag[i];
1958: n = a->i[i + 1] - diag[i] - 1;
1959: idx = a->j + diag[i] + 1;
1960: v = aa + diag[i] + 1;
1961: sum = b[i] * d / omega;
1962: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1963: x[i] = sum;
1964: }
1965: PetscCall(VecRestoreArray(xx, &x));
1966: PetscCall(VecRestoreArrayRead(bb, &b));
1967: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1968: PetscCall(PetscLogFlops(a->nz));
1969: PetscFunctionReturn(PETSC_SUCCESS);
1970: }
1972: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1973: if (flag & SOR_EISENSTAT) {
1974: /* Let A = L + U + D; where L is lower triangular,
1975: U is upper triangular, E = D/omega; This routine applies
1977: (L + E)^{-1} A (U + E)^{-1}
1979: to a vector efficiently using Eisenstat's trick.
1980: */
1981: scale = (2.0 / omega) - 1.0;
1983: /* x = (E + U)^{-1} b */
1984: for (i = m - 1; i >= 0; i--) {
1985: n = a->i[i + 1] - diag[i] - 1;
1986: idx = a->j + diag[i] + 1;
1987: v = aa + diag[i] + 1;
1988: sum = b[i];
1989: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1990: x[i] = sum * idiag[i];
1991: }
1993: /* t = b - (2*E - D)x */
1994: v = aa;
1995: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
1997: /* t = (E + L)^{-1}t */
1998: ts = t;
1999: diag = a->diag;
2000: for (i = 0; i < m; i++) {
2001: n = diag[i] - a->i[i];
2002: idx = a->j + a->i[i];
2003: v = aa + a->i[i];
2004: sum = t[i];
2005: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2006: t[i] = sum * idiag[i];
2007: /* x = x + t */
2008: x[i] += t[i];
2009: }
2011: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2012: PetscCall(VecRestoreArray(xx, &x));
2013: PetscCall(VecRestoreArrayRead(bb, &b));
2014: PetscFunctionReturn(PETSC_SUCCESS);
2015: }
2016: if (flag & SOR_ZERO_INITIAL_GUESS) {
2017: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2018: for (i = 0; i < m; i++) {
2019: n = diag[i] - a->i[i];
2020: idx = a->j + a->i[i];
2021: v = aa + a->i[i];
2022: sum = b[i];
2023: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2024: t[i] = sum;
2025: x[i] = sum * idiag[i];
2026: }
2027: xb = t;
2028: PetscCall(PetscLogFlops(a->nz));
2029: } else xb = b;
2030: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2031: for (i = m - 1; i >= 0; i--) {
2032: n = a->i[i + 1] - diag[i] - 1;
2033: idx = a->j + diag[i] + 1;
2034: v = aa + diag[i] + 1;
2035: sum = xb[i];
2036: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2037: if (xb == b) {
2038: x[i] = sum * idiag[i];
2039: } else {
2040: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2041: }
2042: }
2043: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2044: }
2045: its--;
2046: }
2047: while (its--) {
2048: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2049: for (i = 0; i < m; i++) {
2050: /* lower */
2051: n = diag[i] - a->i[i];
2052: idx = a->j + a->i[i];
2053: v = aa + a->i[i];
2054: sum = b[i];
2055: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2056: t[i] = sum; /* save application of the lower-triangular part */
2057: /* upper */
2058: n = a->i[i + 1] - diag[i] - 1;
2059: idx = a->j + diag[i] + 1;
2060: v = aa + diag[i] + 1;
2061: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2062: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2063: }
2064: xb = t;
2065: PetscCall(PetscLogFlops(2.0 * a->nz));
2066: } else xb = b;
2067: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2068: for (i = m - 1; i >= 0; i--) {
2069: sum = xb[i];
2070: if (xb == b) {
2071: /* whole matrix (no checkpointing available) */
2072: n = a->i[i + 1] - a->i[i];
2073: idx = a->j + a->i[i];
2074: v = aa + a->i[i];
2075: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2076: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2077: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2078: n = a->i[i + 1] - diag[i] - 1;
2079: idx = a->j + diag[i] + 1;
2080: v = aa + diag[i] + 1;
2081: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2082: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2083: }
2084: }
2085: if (xb == b) {
2086: PetscCall(PetscLogFlops(2.0 * a->nz));
2087: } else {
2088: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2089: }
2090: }
2091: }
2092: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2093: PetscCall(VecRestoreArray(xx, &x));
2094: PetscCall(VecRestoreArrayRead(bb, &b));
2095: PetscFunctionReturn(PETSC_SUCCESS);
2096: }
2098: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2099: {
2100: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2102: PetscFunctionBegin;
2103: info->block_size = 1.0;
2104: info->nz_allocated = a->maxnz;
2105: info->nz_used = a->nz;
2106: info->nz_unneeded = (a->maxnz - a->nz);
2107: info->assemblies = A->num_ass;
2108: info->mallocs = A->info.mallocs;
2109: info->memory = 0; /* REVIEW ME */
2110: if (A->factortype) {
2111: info->fill_ratio_given = A->info.fill_ratio_given;
2112: info->fill_ratio_needed = A->info.fill_ratio_needed;
2113: info->factor_mallocs = A->info.factor_mallocs;
2114: } else {
2115: info->fill_ratio_given = 0;
2116: info->fill_ratio_needed = 0;
2117: info->factor_mallocs = 0;
2118: }
2119: PetscFunctionReturn(PETSC_SUCCESS);
2120: }
2122: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2123: {
2124: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2125: PetscInt i, m = A->rmap->n - 1;
2126: const PetscScalar *xx;
2127: PetscScalar *bb, *aa;
2128: PetscInt d = 0;
2130: PetscFunctionBegin;
2131: if (x && b) {
2132: PetscCall(VecGetArrayRead(x, &xx));
2133: PetscCall(VecGetArray(b, &bb));
2134: for (i = 0; i < N; i++) {
2135: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2136: if (rows[i] >= A->cmap->n) continue;
2137: bb[rows[i]] = diag * xx[rows[i]];
2138: }
2139: PetscCall(VecRestoreArrayRead(x, &xx));
2140: PetscCall(VecRestoreArray(b, &bb));
2141: }
2143: PetscCall(MatSeqAIJGetArray(A, &aa));
2144: if (a->keepnonzeropattern) {
2145: for (i = 0; i < N; i++) {
2146: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2147: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2148: }
2149: if (diag != 0.0) {
2150: for (i = 0; i < N; i++) {
2151: d = rows[i];
2152: if (rows[i] >= A->cmap->n) continue;
2153: 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);
2154: }
2155: for (i = 0; i < N; i++) {
2156: if (rows[i] >= A->cmap->n) continue;
2157: aa[a->diag[rows[i]]] = diag;
2158: }
2159: }
2160: } else {
2161: if (diag != 0.0) {
2162: for (i = 0; i < N; i++) {
2163: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2164: if (a->ilen[rows[i]] > 0) {
2165: if (rows[i] >= A->cmap->n) {
2166: a->ilen[rows[i]] = 0;
2167: } else {
2168: a->ilen[rows[i]] = 1;
2169: aa[a->i[rows[i]]] = diag;
2170: a->j[a->i[rows[i]]] = rows[i];
2171: }
2172: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2173: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2174: }
2175: }
2176: } else {
2177: for (i = 0; i < N; i++) {
2178: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2179: a->ilen[rows[i]] = 0;
2180: }
2181: }
2182: A->nonzerostate++;
2183: }
2184: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2185: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2186: PetscFunctionReturn(PETSC_SUCCESS);
2187: }
2189: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2190: {
2191: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2192: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2193: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2194: const PetscScalar *xx;
2195: PetscScalar *bb, *aa;
2197: PetscFunctionBegin;
2198: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2199: PetscCall(MatSeqAIJGetArray(A, &aa));
2200: if (x && b) {
2201: PetscCall(VecGetArrayRead(x, &xx));
2202: PetscCall(VecGetArray(b, &bb));
2203: vecs = PETSC_TRUE;
2204: }
2205: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2206: for (i = 0; i < N; i++) {
2207: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2208: PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));
2210: zeroed[rows[i]] = PETSC_TRUE;
2211: }
2212: for (i = 0; i < A->rmap->n; i++) {
2213: if (!zeroed[i]) {
2214: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2215: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2216: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2217: aa[j] = 0.0;
2218: }
2219: }
2220: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2221: }
2222: if (x && b) {
2223: PetscCall(VecRestoreArrayRead(x, &xx));
2224: PetscCall(VecRestoreArray(b, &bb));
2225: }
2226: PetscCall(PetscFree(zeroed));
2227: if (diag != 0.0) {
2228: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2229: if (missing) {
2230: for (i = 0; i < N; i++) {
2231: if (rows[i] >= A->cmap->N) continue;
2232: 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]);
2233: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2234: }
2235: } else {
2236: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2237: }
2238: }
2239: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2240: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2241: PetscFunctionReturn(PETSC_SUCCESS);
2242: }
2244: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2245: {
2246: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2247: const PetscScalar *aa;
2249: PetscFunctionBegin;
2250: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2251: *nz = a->i[row + 1] - a->i[row];
2252: if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2253: if (idx) {
2254: if (*nz && a->j) *idx = a->j + a->i[row];
2255: else *idx = NULL;
2256: }
2257: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2258: PetscFunctionReturn(PETSC_SUCCESS);
2259: }
2261: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2262: {
2263: PetscFunctionBegin;
2264: PetscFunctionReturn(PETSC_SUCCESS);
2265: }
2267: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2268: {
2269: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2270: const MatScalar *v;
2271: PetscReal sum = 0.0;
2272: PetscInt i, j;
2274: PetscFunctionBegin;
2275: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2276: if (type == NORM_FROBENIUS) {
2277: #if defined(PETSC_USE_REAL___FP16)
2278: PetscBLASInt one = 1, nz = a->nz;
2279: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2280: #else
2281: for (i = 0; i < a->nz; i++) {
2282: sum += PetscRealPart(PetscConj(*v) * (*v));
2283: v++;
2284: }
2285: *nrm = PetscSqrtReal(sum);
2286: #endif
2287: PetscCall(PetscLogFlops(2.0 * a->nz));
2288: } else if (type == NORM_1) {
2289: PetscReal *tmp;
2290: PetscInt *jj = a->j;
2291: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2292: *nrm = 0.0;
2293: for (j = 0; j < a->nz; j++) {
2294: tmp[*jj++] += PetscAbsScalar(*v);
2295: v++;
2296: }
2297: for (j = 0; j < A->cmap->n; j++) {
2298: if (tmp[j] > *nrm) *nrm = tmp[j];
2299: }
2300: PetscCall(PetscFree(tmp));
2301: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2302: } else if (type == NORM_INFINITY) {
2303: *nrm = 0.0;
2304: for (j = 0; j < A->rmap->n; j++) {
2305: const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2306: sum = 0.0;
2307: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2308: sum += PetscAbsScalar(*v2);
2309: v2++;
2310: }
2311: if (sum > *nrm) *nrm = sum;
2312: }
2313: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2314: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2315: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2316: PetscFunctionReturn(PETSC_SUCCESS);
2317: }
2319: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2320: {
2321: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2322: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2323: const MatScalar *va, *vb;
2324: PetscInt ma, na, mb, nb, i;
2326: PetscFunctionBegin;
2327: PetscCall(MatGetSize(A, &ma, &na));
2328: PetscCall(MatGetSize(B, &mb, &nb));
2329: if (ma != nb || na != mb) {
2330: *f = PETSC_FALSE;
2331: PetscFunctionReturn(PETSC_SUCCESS);
2332: }
2333: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2334: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2335: aii = aij->i;
2336: bii = bij->i;
2337: adx = aij->j;
2338: bdx = bij->j;
2339: PetscCall(PetscMalloc1(ma, &aptr));
2340: PetscCall(PetscMalloc1(mb, &bptr));
2341: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2342: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2344: *f = PETSC_TRUE;
2345: for (i = 0; i < ma; i++) {
2346: while (aptr[i] < aii[i + 1]) {
2347: PetscInt idc, idr;
2348: PetscScalar vc, vr;
2349: /* column/row index/value */
2350: idc = adx[aptr[i]];
2351: idr = bdx[bptr[idc]];
2352: vc = va[aptr[i]];
2353: vr = vb[bptr[idc]];
2354: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2355: *f = PETSC_FALSE;
2356: goto done;
2357: } else {
2358: aptr[i]++;
2359: if (B || i != idc) bptr[idc]++;
2360: }
2361: }
2362: }
2363: done:
2364: PetscCall(PetscFree(aptr));
2365: PetscCall(PetscFree(bptr));
2366: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2367: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2368: PetscFunctionReturn(PETSC_SUCCESS);
2369: }
2371: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2372: {
2373: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2374: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2375: MatScalar *va, *vb;
2376: PetscInt ma, na, mb, nb, i;
2378: PetscFunctionBegin;
2379: PetscCall(MatGetSize(A, &ma, &na));
2380: PetscCall(MatGetSize(B, &mb, &nb));
2381: if (ma != nb || na != mb) {
2382: *f = PETSC_FALSE;
2383: PetscFunctionReturn(PETSC_SUCCESS);
2384: }
2385: aii = aij->i;
2386: bii = bij->i;
2387: adx = aij->j;
2388: bdx = bij->j;
2389: va = aij->a;
2390: vb = bij->a;
2391: PetscCall(PetscMalloc1(ma, &aptr));
2392: PetscCall(PetscMalloc1(mb, &bptr));
2393: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2394: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2396: *f = PETSC_TRUE;
2397: for (i = 0; i < ma; i++) {
2398: while (aptr[i] < aii[i + 1]) {
2399: PetscInt idc, idr;
2400: PetscScalar vc, vr;
2401: /* column/row index/value */
2402: idc = adx[aptr[i]];
2403: idr = bdx[bptr[idc]];
2404: vc = va[aptr[i]];
2405: vr = vb[bptr[idc]];
2406: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2407: *f = PETSC_FALSE;
2408: goto done;
2409: } else {
2410: aptr[i]++;
2411: if (B || i != idc) bptr[idc]++;
2412: }
2413: }
2414: }
2415: done:
2416: PetscCall(PetscFree(aptr));
2417: PetscCall(PetscFree(bptr));
2418: PetscFunctionReturn(PETSC_SUCCESS);
2419: }
2421: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2422: {
2423: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2424: const PetscScalar *l, *r;
2425: PetscScalar x;
2426: MatScalar *v;
2427: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2428: const PetscInt *jj;
2430: PetscFunctionBegin;
2431: if (ll) {
2432: /* The local size is used so that VecMPI can be passed to this routine
2433: by MatDiagonalScale_MPIAIJ */
2434: PetscCall(VecGetLocalSize(ll, &m));
2435: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2436: PetscCall(VecGetArrayRead(ll, &l));
2437: PetscCall(MatSeqAIJGetArray(A, &v));
2438: for (i = 0; i < m; i++) {
2439: x = l[i];
2440: M = a->i[i + 1] - a->i[i];
2441: for (j = 0; j < M; j++) (*v++) *= x;
2442: }
2443: PetscCall(VecRestoreArrayRead(ll, &l));
2444: PetscCall(PetscLogFlops(nz));
2445: PetscCall(MatSeqAIJRestoreArray(A, &v));
2446: }
2447: if (rr) {
2448: PetscCall(VecGetLocalSize(rr, &n));
2449: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2450: PetscCall(VecGetArrayRead(rr, &r));
2451: PetscCall(MatSeqAIJGetArray(A, &v));
2452: jj = a->j;
2453: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2454: PetscCall(MatSeqAIJRestoreArray(A, &v));
2455: PetscCall(VecRestoreArrayRead(rr, &r));
2456: PetscCall(PetscLogFlops(nz));
2457: }
2458: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2459: PetscFunctionReturn(PETSC_SUCCESS);
2460: }
2462: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2463: {
2464: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2465: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2466: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2467: const PetscInt *irow, *icol;
2468: const PetscScalar *aa;
2469: PetscInt nrows, ncols;
2470: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2471: MatScalar *a_new, *mat_a, *c_a;
2472: Mat C;
2473: PetscBool stride;
2475: PetscFunctionBegin;
2476: PetscCall(ISGetIndices(isrow, &irow));
2477: PetscCall(ISGetLocalSize(isrow, &nrows));
2478: PetscCall(ISGetLocalSize(iscol, &ncols));
2480: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2481: if (stride) {
2482: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2483: } else {
2484: first = 0;
2485: step = 0;
2486: }
2487: if (stride && step == 1) {
2488: /* special case of contiguous rows */
2489: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2490: /* loop over new rows determining lens and starting points */
2491: for (i = 0; i < nrows; i++) {
2492: kstart = ai[irow[i]];
2493: kend = kstart + ailen[irow[i]];
2494: starts[i] = kstart;
2495: for (k = kstart; k < kend; k++) {
2496: if (aj[k] >= first) {
2497: starts[i] = k;
2498: break;
2499: }
2500: }
2501: sum = 0;
2502: while (k < kend) {
2503: if (aj[k++] >= first + ncols) break;
2504: sum++;
2505: }
2506: lens[i] = sum;
2507: }
2508: /* create submatrix */
2509: if (scall == MAT_REUSE_MATRIX) {
2510: PetscInt n_cols, n_rows;
2511: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2512: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2513: PetscCall(MatZeroEntries(*B));
2514: C = *B;
2515: } else {
2516: PetscInt rbs, cbs;
2517: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2518: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2519: PetscCall(ISGetBlockSize(isrow, &rbs));
2520: PetscCall(ISGetBlockSize(iscol, &cbs));
2521: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2522: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2523: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2524: }
2525: c = (Mat_SeqAIJ *)C->data;
2527: /* loop over rows inserting into submatrix */
2528: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2529: j_new = c->j;
2530: i_new = c->i;
2531: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2532: for (i = 0; i < nrows; i++) {
2533: ii = starts[i];
2534: lensi = lens[i];
2535: if (lensi) {
2536: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2537: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2538: a_new += lensi;
2539: }
2540: i_new[i + 1] = i_new[i] + lensi;
2541: c->ilen[i] = lensi;
2542: }
2543: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2544: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2545: PetscCall(PetscFree2(lens, starts));
2546: } else {
2547: PetscCall(ISGetIndices(iscol, &icol));
2548: PetscCall(PetscCalloc1(oldcols, &smap));
2549: PetscCall(PetscMalloc1(1 + nrows, &lens));
2550: for (i = 0; i < ncols; i++) {
2551: 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);
2552: smap[icol[i]] = i + 1;
2553: }
2555: /* determine lens of each row */
2556: for (i = 0; i < nrows; i++) {
2557: kstart = ai[irow[i]];
2558: kend = kstart + a->ilen[irow[i]];
2559: lens[i] = 0;
2560: for (k = kstart; k < kend; k++) {
2561: if (smap[aj[k]]) lens[i]++;
2562: }
2563: }
2564: /* Create and fill new matrix */
2565: if (scall == MAT_REUSE_MATRIX) {
2566: PetscBool equal;
2568: c = (Mat_SeqAIJ *)((*B)->data);
2569: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2570: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2571: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2572: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2573: C = *B;
2574: } else {
2575: PetscInt rbs, cbs;
2576: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2577: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2578: PetscCall(ISGetBlockSize(isrow, &rbs));
2579: PetscCall(ISGetBlockSize(iscol, &cbs));
2580: if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2581: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2582: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2583: }
2584: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2586: c = (Mat_SeqAIJ *)C->data;
2587: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2588: for (i = 0; i < nrows; i++) {
2589: row = irow[i];
2590: kstart = ai[row];
2591: kend = kstart + a->ilen[row];
2592: mat_i = c->i[i];
2593: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2594: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2595: mat_ilen = c->ilen + i;
2596: for (k = kstart; k < kend; k++) {
2597: if ((tcol = smap[a->j[k]])) {
2598: *mat_j++ = tcol - 1;
2599: *mat_a++ = aa[k];
2600: (*mat_ilen)++;
2601: }
2602: }
2603: }
2604: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2605: /* Free work space */
2606: PetscCall(ISRestoreIndices(iscol, &icol));
2607: PetscCall(PetscFree(smap));
2608: PetscCall(PetscFree(lens));
2609: /* sort */
2610: for (i = 0; i < nrows; i++) {
2611: PetscInt ilen;
2613: mat_i = c->i[i];
2614: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2615: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2616: ilen = c->ilen[i];
2617: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2618: }
2619: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2620: }
2621: #if defined(PETSC_HAVE_DEVICE)
2622: PetscCall(MatBindToCPU(C, A->boundtocpu));
2623: #endif
2624: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2625: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2627: PetscCall(ISRestoreIndices(isrow, &irow));
2628: *B = C;
2629: PetscFunctionReturn(PETSC_SUCCESS);
2630: }
2632: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2633: {
2634: Mat B;
2636: PetscFunctionBegin;
2637: if (scall == MAT_INITIAL_MATRIX) {
2638: PetscCall(MatCreate(subComm, &B));
2639: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2640: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2641: PetscCall(MatSetType(B, MATSEQAIJ));
2642: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2643: *subMat = B;
2644: } else {
2645: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2646: }
2647: PetscFunctionReturn(PETSC_SUCCESS);
2648: }
2650: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2651: {
2652: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2653: Mat outA;
2654: PetscBool row_identity, col_identity;
2656: PetscFunctionBegin;
2657: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2659: PetscCall(ISIdentity(row, &row_identity));
2660: PetscCall(ISIdentity(col, &col_identity));
2662: outA = inA;
2663: outA->factortype = MAT_FACTOR_LU;
2664: PetscCall(PetscFree(inA->solvertype));
2665: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2667: PetscCall(PetscObjectReference((PetscObject)row));
2668: PetscCall(ISDestroy(&a->row));
2670: a->row = row;
2672: PetscCall(PetscObjectReference((PetscObject)col));
2673: PetscCall(ISDestroy(&a->col));
2675: a->col = col;
2677: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2678: PetscCall(ISDestroy(&a->icol));
2679: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2681: if (!a->solve_work) { /* this matrix may have been factored before */
2682: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2683: }
2685: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2686: if (row_identity && col_identity) {
2687: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2688: } else {
2689: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2690: }
2691: PetscFunctionReturn(PETSC_SUCCESS);
2692: }
2694: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2695: {
2696: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2697: PetscScalar *v;
2698: PetscBLASInt one = 1, bnz;
2700: PetscFunctionBegin;
2701: PetscCall(MatSeqAIJGetArray(inA, &v));
2702: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2703: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2704: PetscCall(PetscLogFlops(a->nz));
2705: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2706: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2707: PetscFunctionReturn(PETSC_SUCCESS);
2708: }
2710: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2711: {
2712: PetscInt i;
2714: PetscFunctionBegin;
2715: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2716: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2718: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2719: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2721: if (submatj->rbuf1) {
2722: PetscCall(PetscFree(submatj->rbuf1[0]));
2723: PetscCall(PetscFree(submatj->rbuf1));
2724: }
2726: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2727: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2728: PetscCall(PetscFree(submatj->pa));
2729: }
2731: #if defined(PETSC_USE_CTABLE)
2732: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2733: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2734: PetscCall(PetscFree(submatj->rmap_loc));
2735: #else
2736: PetscCall(PetscFree(submatj->rmap));
2737: #endif
2739: if (!submatj->allcolumns) {
2740: #if defined(PETSC_USE_CTABLE)
2741: PetscCall(PetscHMapIDestroy(&submatj->cmap));
2742: #else
2743: PetscCall(PetscFree(submatj->cmap));
2744: #endif
2745: }
2746: PetscCall(PetscFree(submatj->row2proc));
2748: PetscCall(PetscFree(submatj));
2749: PetscFunctionReturn(PETSC_SUCCESS);
2750: }
2752: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2753: {
2754: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2755: Mat_SubSppt *submatj = c->submatis1;
2757: PetscFunctionBegin;
2758: PetscCall((*submatj->destroy)(C));
2759: PetscCall(MatDestroySubMatrix_Private(submatj));
2760: PetscFunctionReturn(PETSC_SUCCESS);
2761: }
2763: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2764: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2765: {
2766: PetscInt i;
2767: Mat C;
2768: Mat_SeqAIJ *c;
2769: Mat_SubSppt *submatj;
2771: PetscFunctionBegin;
2772: for (i = 0; i < n; i++) {
2773: C = (*mat)[i];
2774: c = (Mat_SeqAIJ *)C->data;
2775: submatj = c->submatis1;
2776: if (submatj) {
2777: if (--((PetscObject)C)->refct <= 0) {
2778: PetscCall(PetscFree(C->factorprefix));
2779: PetscCall((*submatj->destroy)(C));
2780: PetscCall(MatDestroySubMatrix_Private(submatj));
2781: PetscCall(PetscFree(C->defaultvectype));
2782: PetscCall(PetscFree(C->defaultrandtype));
2783: PetscCall(PetscLayoutDestroy(&C->rmap));
2784: PetscCall(PetscLayoutDestroy(&C->cmap));
2785: PetscCall(PetscHeaderDestroy(&C));
2786: }
2787: } else {
2788: PetscCall(MatDestroy(&C));
2789: }
2790: }
2792: /* Destroy Dummy submatrices created for reuse */
2793: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2795: PetscCall(PetscFree(*mat));
2796: PetscFunctionReturn(PETSC_SUCCESS);
2797: }
2799: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2800: {
2801: PetscInt i;
2803: PetscFunctionBegin;
2804: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2806: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2807: PetscFunctionReturn(PETSC_SUCCESS);
2808: }
2810: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2811: {
2812: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2813: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2814: const PetscInt *idx;
2815: PetscInt start, end, *ai, *aj, bs = A->rmap->bs == A->cmap->bs ? A->rmap->bs : 1;
2816: PetscBT table;
2818: PetscFunctionBegin;
2819: m = A->rmap->n / bs;
2820: ai = a->i;
2821: aj = a->j;
2823: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2825: PetscCall(PetscMalloc1(m + 1, &nidx));
2826: PetscCall(PetscBTCreate(m, &table));
2828: for (i = 0; i < is_max; i++) {
2829: /* Initialize the two local arrays */
2830: isz = 0;
2831: PetscCall(PetscBTMemzero(m, table));
2833: /* Extract the indices, assume there can be duplicate entries */
2834: PetscCall(ISGetIndices(is[i], &idx));
2835: PetscCall(ISGetLocalSize(is[i], &n));
2837: if (bs > 1) {
2838: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2839: for (j = 0; j < n; ++j) {
2840: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2841: }
2842: PetscCall(ISRestoreIndices(is[i], &idx));
2843: PetscCall(ISDestroy(&is[i]));
2845: k = 0;
2846: for (j = 0; j < ov; j++) { /* for each overlap */
2847: n = isz;
2848: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2849: for (ll = 0; ll < bs; ll++) {
2850: row = bs * nidx[k] + ll;
2851: start = ai[row];
2852: end = ai[row + 1];
2853: for (l = start; l < end; l++) {
2854: val = aj[l] / bs;
2855: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2856: }
2857: }
2858: }
2859: }
2860: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, is + i));
2861: } else {
2862: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2863: for (j = 0; j < n; ++j) {
2864: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2865: }
2866: PetscCall(ISRestoreIndices(is[i], &idx));
2867: PetscCall(ISDestroy(&is[i]));
2869: k = 0;
2870: for (j = 0; j < ov; j++) { /* for each overlap */
2871: n = isz;
2872: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2873: row = nidx[k];
2874: start = ai[row];
2875: end = ai[row + 1];
2876: for (l = start; l < end; l++) {
2877: val = aj[l];
2878: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2879: }
2880: }
2881: }
2882: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, is + i));
2883: }
2884: }
2885: PetscCall(PetscBTDestroy(&table));
2886: PetscCall(PetscFree(nidx));
2887: PetscFunctionReturn(PETSC_SUCCESS);
2888: }
2890: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2891: {
2892: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2893: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2894: const PetscInt *row, *col;
2895: PetscInt *cnew, j, *lens;
2896: IS icolp, irowp;
2897: PetscInt *cwork = NULL;
2898: PetscScalar *vwork = NULL;
2900: PetscFunctionBegin;
2901: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2902: PetscCall(ISGetIndices(irowp, &row));
2903: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2904: PetscCall(ISGetIndices(icolp, &col));
2906: /* determine lengths of permuted rows */
2907: PetscCall(PetscMalloc1(m + 1, &lens));
2908: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2909: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2910: PetscCall(MatSetSizes(*B, m, n, m, n));
2911: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2912: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2913: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2914: PetscCall(PetscFree(lens));
2916: PetscCall(PetscMalloc1(n, &cnew));
2917: for (i = 0; i < m; i++) {
2918: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2919: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2920: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2921: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2922: }
2923: PetscCall(PetscFree(cnew));
2925: (*B)->assembled = PETSC_FALSE;
2927: #if defined(PETSC_HAVE_DEVICE)
2928: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2929: #endif
2930: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2931: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2932: PetscCall(ISRestoreIndices(irowp, &row));
2933: PetscCall(ISRestoreIndices(icolp, &col));
2934: PetscCall(ISDestroy(&irowp));
2935: PetscCall(ISDestroy(&icolp));
2936: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2937: PetscFunctionReturn(PETSC_SUCCESS);
2938: }
2940: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2941: {
2942: PetscFunctionBegin;
2943: /* If the two matrices have the same copy implementation, use fast copy. */
2944: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2945: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2946: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2947: const PetscScalar *aa;
2948: PetscScalar *bb;
2950: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2951: PetscCall(MatSeqAIJGetArrayWrite(B, &bb));
2953: 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]);
2954: PetscCall(PetscArraycpy(bb, aa, a->i[A->rmap->n]));
2955: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2956: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2957: PetscCall(MatSeqAIJRestoreArrayWrite(B, &bb));
2958: } else {
2959: PetscCall(MatCopy_Basic(A, B, str));
2960: }
2961: PetscFunctionReturn(PETSC_SUCCESS);
2962: }
2964: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2965: {
2966: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2968: PetscFunctionBegin;
2969: *array = a->a;
2970: PetscFunctionReturn(PETSC_SUCCESS);
2971: }
2973: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2974: {
2975: PetscFunctionBegin;
2976: *array = NULL;
2977: PetscFunctionReturn(PETSC_SUCCESS);
2978: }
2980: /*
2981: Computes the number of nonzeros per row needed for preallocation when X and Y
2982: have different nonzero structure.
2983: */
2984: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2985: {
2986: PetscInt i, j, k, nzx, nzy;
2988: PetscFunctionBegin;
2989: /* Set the number of nonzeros in the new matrix */
2990: for (i = 0; i < m; i++) {
2991: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2992: nzx = xi[i + 1] - xi[i];
2993: nzy = yi[i + 1] - yi[i];
2994: nnz[i] = 0;
2995: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2996: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
2997: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
2998: nnz[i]++;
2999: }
3000: for (; k < nzy; k++) nnz[i]++;
3001: }
3002: PetscFunctionReturn(PETSC_SUCCESS);
3003: }
3005: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3006: {
3007: PetscInt m = Y->rmap->N;
3008: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3009: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3011: PetscFunctionBegin;
3012: /* Set the number of nonzeros in the new matrix */
3013: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3014: PetscFunctionReturn(PETSC_SUCCESS);
3015: }
3017: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3018: {
3019: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3021: PetscFunctionBegin;
3022: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3023: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3024: if (e) {
3025: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3026: if (e) {
3027: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3028: if (e) str = SAME_NONZERO_PATTERN;
3029: }
3030: }
3031: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3032: }
3033: if (str == SAME_NONZERO_PATTERN) {
3034: const PetscScalar *xa;
3035: PetscScalar *ya, alpha = a;
3036: PetscBLASInt one = 1, bnz;
3038: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3039: PetscCall(MatSeqAIJGetArray(Y, &ya));
3040: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3041: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3042: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3043: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3044: PetscCall(PetscLogFlops(2.0 * bnz));
3045: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3046: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3047: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3048: PetscCall(MatAXPY_Basic(Y, a, X, str));
3049: } else {
3050: Mat B;
3051: PetscInt *nnz;
3052: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3053: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3054: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3055: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3056: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3057: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3058: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3059: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3060: PetscCall(MatHeaderMerge(Y, &B));
3061: PetscCall(MatSeqAIJCheckInode(Y));
3062: PetscCall(PetscFree(nnz));
3063: }
3064: PetscFunctionReturn(PETSC_SUCCESS);
3065: }
3067: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3068: {
3069: #if defined(PETSC_USE_COMPLEX)
3070: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3071: PetscInt i, nz;
3072: PetscScalar *a;
3074: PetscFunctionBegin;
3075: nz = aij->nz;
3076: PetscCall(MatSeqAIJGetArray(mat, &a));
3077: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3078: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3079: #else
3080: PetscFunctionBegin;
3081: #endif
3082: PetscFunctionReturn(PETSC_SUCCESS);
3083: }
3085: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3086: {
3087: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3088: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3089: PetscReal atmp;
3090: PetscScalar *x;
3091: const MatScalar *aa, *av;
3093: PetscFunctionBegin;
3094: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3095: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3096: aa = av;
3097: ai = a->i;
3098: aj = a->j;
3100: PetscCall(VecGetArrayWrite(v, &x));
3101: PetscCall(VecGetLocalSize(v, &n));
3102: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3103: for (i = 0; i < m; i++) {
3104: ncols = ai[1] - ai[0];
3105: ai++;
3106: x[i] = 0;
3107: for (j = 0; j < ncols; j++) {
3108: atmp = PetscAbsScalar(*aa);
3109: if (PetscAbsScalar(x[i]) < atmp) {
3110: x[i] = atmp;
3111: if (idx) idx[i] = *aj;
3112: }
3113: aa++;
3114: aj++;
3115: }
3116: }
3117: PetscCall(VecRestoreArrayWrite(v, &x));
3118: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3119: PetscFunctionReturn(PETSC_SUCCESS);
3120: }
3122: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3123: {
3124: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3125: PetscInt i, j, m = A->rmap->n, *ai, ncols, n;
3126: PetscScalar *x;
3127: const MatScalar *aa, *av;
3129: PetscFunctionBegin;
3130: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3131: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3132: aa = av;
3133: ai = a->i;
3135: PetscCall(VecGetArrayWrite(v, &x));
3136: PetscCall(VecGetLocalSize(v, &n));
3137: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3138: for (i = 0; i < m; i++) {
3139: ncols = ai[1] - ai[0];
3140: ai++;
3141: x[i] = 0;
3142: for (j = 0; j < ncols; j++) {
3143: x[i] += PetscAbsScalar(*aa);
3144: aa++;
3145: }
3146: }
3147: PetscCall(VecRestoreArrayWrite(v, &x));
3148: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3149: PetscFunctionReturn(PETSC_SUCCESS);
3150: }
3152: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3153: {
3154: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3155: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3156: PetscScalar *x;
3157: const MatScalar *aa, *av;
3159: PetscFunctionBegin;
3160: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3161: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3162: aa = av;
3163: ai = a->i;
3164: aj = a->j;
3166: PetscCall(VecGetArrayWrite(v, &x));
3167: PetscCall(VecGetLocalSize(v, &n));
3168: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3169: for (i = 0; i < m; i++) {
3170: ncols = ai[1] - ai[0];
3171: ai++;
3172: if (ncols == A->cmap->n) { /* row is dense */
3173: x[i] = *aa;
3174: if (idx) idx[i] = 0;
3175: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3176: x[i] = 0.0;
3177: if (idx) {
3178: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3179: if (aj[j] > j) {
3180: idx[i] = j;
3181: break;
3182: }
3183: }
3184: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3185: if (j == ncols && j < A->cmap->n) idx[i] = j;
3186: }
3187: }
3188: for (j = 0; j < ncols; j++) {
3189: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3190: x[i] = *aa;
3191: if (idx) idx[i] = *aj;
3192: }
3193: aa++;
3194: aj++;
3195: }
3196: }
3197: PetscCall(VecRestoreArrayWrite(v, &x));
3198: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3199: PetscFunctionReturn(PETSC_SUCCESS);
3200: }
3202: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3203: {
3204: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3205: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3206: PetscScalar *x;
3207: const MatScalar *aa, *av;
3209: PetscFunctionBegin;
3210: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3211: aa = av;
3212: ai = a->i;
3213: aj = a->j;
3215: PetscCall(VecGetArrayWrite(v, &x));
3216: PetscCall(VecGetLocalSize(v, &n));
3217: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3218: for (i = 0; i < m; i++) {
3219: ncols = ai[1] - ai[0];
3220: ai++;
3221: if (ncols == A->cmap->n) { /* row is dense */
3222: x[i] = *aa;
3223: if (idx) idx[i] = 0;
3224: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3225: x[i] = 0.0;
3226: if (idx) { /* find first implicit 0.0 in the row */
3227: for (j = 0; j < ncols; j++) {
3228: if (aj[j] > j) {
3229: idx[i] = j;
3230: break;
3231: }
3232: }
3233: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3234: if (j == ncols && j < A->cmap->n) idx[i] = j;
3235: }
3236: }
3237: for (j = 0; j < ncols; j++) {
3238: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3239: x[i] = *aa;
3240: if (idx) idx[i] = *aj;
3241: }
3242: aa++;
3243: aj++;
3244: }
3245: }
3246: PetscCall(VecRestoreArrayWrite(v, &x));
3247: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3248: PetscFunctionReturn(PETSC_SUCCESS);
3249: }
3251: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3252: {
3253: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3254: PetscInt i, j, m = A->rmap->n, ncols, n;
3255: const PetscInt *ai, *aj;
3256: PetscScalar *x;
3257: const MatScalar *aa, *av;
3259: PetscFunctionBegin;
3260: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3261: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3262: aa = av;
3263: ai = a->i;
3264: aj = a->j;
3266: PetscCall(VecGetArrayWrite(v, &x));
3267: PetscCall(VecGetLocalSize(v, &n));
3268: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3269: for (i = 0; i < m; i++) {
3270: ncols = ai[1] - ai[0];
3271: ai++;
3272: if (ncols == A->cmap->n) { /* row is dense */
3273: x[i] = *aa;
3274: if (idx) idx[i] = 0;
3275: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3276: x[i] = 0.0;
3277: if (idx) { /* find first implicit 0.0 in the row */
3278: for (j = 0; j < ncols; j++) {
3279: if (aj[j] > j) {
3280: idx[i] = j;
3281: break;
3282: }
3283: }
3284: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3285: if (j == ncols && j < A->cmap->n) idx[i] = j;
3286: }
3287: }
3288: for (j = 0; j < ncols; j++) {
3289: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3290: x[i] = *aa;
3291: if (idx) idx[i] = *aj;
3292: }
3293: aa++;
3294: aj++;
3295: }
3296: }
3297: PetscCall(VecRestoreArrayWrite(v, &x));
3298: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3299: PetscFunctionReturn(PETSC_SUCCESS);
3300: }
3302: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3303: {
3304: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3305: PetscInt i, bs = A->rmap->bs, mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3306: MatScalar *diag, work[25], *v_work;
3307: const PetscReal shift = 0.0;
3308: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3310: PetscFunctionBegin;
3311: allowzeropivot = PetscNot(A->erroriffailure);
3312: if (a->ibdiagvalid) {
3313: if (values) *values = a->ibdiag;
3314: PetscFunctionReturn(PETSC_SUCCESS);
3315: }
3316: PetscCall(MatMarkDiagonal_SeqAIJ(A));
3317: if (!a->ibdiag) PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag));
3318: diag = a->ibdiag;
3319: if (values) *values = a->ibdiag;
3320: /* factor and invert each block */
3321: switch (bs) {
3322: case 1:
3323: for (i = 0; i < mbs; i++) {
3324: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3325: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3326: PetscCheck(allowzeropivot, 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);
3327: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3328: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3329: A->factorerror_zeropivot_row = i;
3330: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3331: }
3332: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3333: }
3334: break;
3335: case 2:
3336: for (i = 0; i < mbs; i++) {
3337: ij[0] = 2 * i;
3338: ij[1] = 2 * i + 1;
3339: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3340: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3341: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3342: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3343: diag += 4;
3344: }
3345: break;
3346: case 3:
3347: for (i = 0; i < mbs; i++) {
3348: ij[0] = 3 * i;
3349: ij[1] = 3 * i + 1;
3350: ij[2] = 3 * i + 2;
3351: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3352: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3353: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3354: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3355: diag += 9;
3356: }
3357: break;
3358: case 4:
3359: for (i = 0; i < mbs; i++) {
3360: ij[0] = 4 * i;
3361: ij[1] = 4 * i + 1;
3362: ij[2] = 4 * i + 2;
3363: ij[3] = 4 * i + 3;
3364: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3365: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3366: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3367: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3368: diag += 16;
3369: }
3370: break;
3371: case 5:
3372: for (i = 0; i < mbs; i++) {
3373: ij[0] = 5 * i;
3374: ij[1] = 5 * i + 1;
3375: ij[2] = 5 * i + 2;
3376: ij[3] = 5 * i + 3;
3377: ij[4] = 5 * i + 4;
3378: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3379: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3380: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3381: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3382: diag += 25;
3383: }
3384: break;
3385: case 6:
3386: for (i = 0; i < mbs; i++) {
3387: ij[0] = 6 * i;
3388: ij[1] = 6 * i + 1;
3389: ij[2] = 6 * i + 2;
3390: ij[3] = 6 * i + 3;
3391: ij[4] = 6 * i + 4;
3392: ij[5] = 6 * i + 5;
3393: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3394: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3395: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3396: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3397: diag += 36;
3398: }
3399: break;
3400: case 7:
3401: for (i = 0; i < mbs; i++) {
3402: ij[0] = 7 * i;
3403: ij[1] = 7 * i + 1;
3404: ij[2] = 7 * i + 2;
3405: ij[3] = 7 * i + 3;
3406: ij[4] = 7 * i + 4;
3407: ij[5] = 7 * i + 5;
3408: ij[6] = 7 * i + 6;
3409: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3410: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3411: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3412: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3413: diag += 49;
3414: }
3415: break;
3416: default:
3417: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3418: for (i = 0; i < mbs; i++) {
3419: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3420: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3421: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3422: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3423: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3424: diag += bs2;
3425: }
3426: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3427: }
3428: a->ibdiagvalid = PETSC_TRUE;
3429: PetscFunctionReturn(PETSC_SUCCESS);
3430: }
3432: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3433: {
3434: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3435: PetscScalar a, *aa;
3436: PetscInt m, n, i, j, col;
3438: PetscFunctionBegin;
3439: if (!x->assembled) {
3440: PetscCall(MatGetSize(x, &m, &n));
3441: for (i = 0; i < m; i++) {
3442: for (j = 0; j < aij->imax[i]; j++) {
3443: PetscCall(PetscRandomGetValue(rctx, &a));
3444: col = (PetscInt)(n * PetscRealPart(a));
3445: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3446: }
3447: }
3448: } else {
3449: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3450: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3451: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3452: }
3453: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3454: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3455: PetscFunctionReturn(PETSC_SUCCESS);
3456: }
3458: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3459: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3460: {
3461: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3462: PetscScalar a;
3463: PetscInt m, n, i, j, col, nskip;
3465: PetscFunctionBegin;
3466: nskip = high - low;
3467: PetscCall(MatGetSize(x, &m, &n));
3468: n -= nskip; /* shrink number of columns where nonzeros can be set */
3469: for (i = 0; i < m; i++) {
3470: for (j = 0; j < aij->imax[i]; j++) {
3471: PetscCall(PetscRandomGetValue(rctx, &a));
3472: col = (PetscInt)(n * PetscRealPart(a));
3473: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3474: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3475: }
3476: }
3477: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3478: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3479: PetscFunctionReturn(PETSC_SUCCESS);
3480: }
3482: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3483: MatGetRow_SeqAIJ,
3484: MatRestoreRow_SeqAIJ,
3485: MatMult_SeqAIJ,
3486: /* 4*/ MatMultAdd_SeqAIJ,
3487: MatMultTranspose_SeqAIJ,
3488: MatMultTransposeAdd_SeqAIJ,
3489: NULL,
3490: NULL,
3491: NULL,
3492: /* 10*/ NULL,
3493: MatLUFactor_SeqAIJ,
3494: NULL,
3495: MatSOR_SeqAIJ,
3496: MatTranspose_SeqAIJ,
3497: /* 15*/ MatGetInfo_SeqAIJ,
3498: MatEqual_SeqAIJ,
3499: MatGetDiagonal_SeqAIJ,
3500: MatDiagonalScale_SeqAIJ,
3501: MatNorm_SeqAIJ,
3502: /* 20*/ NULL,
3503: MatAssemblyEnd_SeqAIJ,
3504: MatSetOption_SeqAIJ,
3505: MatZeroEntries_SeqAIJ,
3506: /* 24*/ MatZeroRows_SeqAIJ,
3507: NULL,
3508: NULL,
3509: NULL,
3510: NULL,
3511: /* 29*/ MatSetUp_Seq_Hash,
3512: NULL,
3513: NULL,
3514: NULL,
3515: NULL,
3516: /* 34*/ MatDuplicate_SeqAIJ,
3517: NULL,
3518: NULL,
3519: MatILUFactor_SeqAIJ,
3520: NULL,
3521: /* 39*/ MatAXPY_SeqAIJ,
3522: MatCreateSubMatrices_SeqAIJ,
3523: MatIncreaseOverlap_SeqAIJ,
3524: MatGetValues_SeqAIJ,
3525: MatCopy_SeqAIJ,
3526: /* 44*/ MatGetRowMax_SeqAIJ,
3527: MatScale_SeqAIJ,
3528: MatShift_SeqAIJ,
3529: MatDiagonalSet_SeqAIJ,
3530: MatZeroRowsColumns_SeqAIJ,
3531: /* 49*/ MatSetRandom_SeqAIJ,
3532: MatGetRowIJ_SeqAIJ,
3533: MatRestoreRowIJ_SeqAIJ,
3534: MatGetColumnIJ_SeqAIJ,
3535: MatRestoreColumnIJ_SeqAIJ,
3536: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3537: NULL,
3538: NULL,
3539: MatPermute_SeqAIJ,
3540: NULL,
3541: /* 59*/ NULL,
3542: MatDestroy_SeqAIJ,
3543: MatView_SeqAIJ,
3544: NULL,
3545: NULL,
3546: /* 64*/ MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3547: NULL,
3548: NULL,
3549: NULL,
3550: MatGetRowMaxAbs_SeqAIJ,
3551: /* 69*/ MatGetRowMinAbs_SeqAIJ,
3552: NULL,
3553: NULL,
3554: MatFDColoringApply_AIJ,
3555: NULL,
3556: /* 74*/ MatFindZeroDiagonals_SeqAIJ,
3557: NULL,
3558: NULL,
3559: NULL,
3560: MatLoad_SeqAIJ,
3561: /* 79*/ NULL,
3562: NULL,
3563: NULL,
3564: NULL,
3565: NULL,
3566: /* 84*/ NULL,
3567: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3568: MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3569: NULL,
3570: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3571: /* 90*/ NULL,
3572: MatProductSetFromOptions_SeqAIJ,
3573: NULL,
3574: NULL,
3575: MatConjugate_SeqAIJ,
3576: /* 94*/ NULL,
3577: MatSetValuesRow_SeqAIJ,
3578: MatRealPart_SeqAIJ,
3579: MatImaginaryPart_SeqAIJ,
3580: NULL,
3581: /* 99*/ NULL,
3582: MatMatSolve_SeqAIJ,
3583: NULL,
3584: MatGetRowMin_SeqAIJ,
3585: NULL,
3586: /*104*/ MatMissingDiagonal_SeqAIJ,
3587: NULL,
3588: NULL,
3589: NULL,
3590: NULL,
3591: /*109*/ NULL,
3592: NULL,
3593: NULL,
3594: NULL,
3595: NULL,
3596: /*114*/ MatGetMultiProcBlock_SeqAIJ,
3597: MatFindNonzeroRows_SeqAIJ,
3598: MatGetColumnReductions_SeqAIJ,
3599: MatInvertBlockDiagonal_SeqAIJ,
3600: MatInvertVariableBlockDiagonal_SeqAIJ,
3601: /*119*/ NULL,
3602: NULL,
3603: NULL,
3604: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3605: MatTransposeColoringCreate_SeqAIJ,
3606: /*124*/ MatTransColoringApplySpToDen_SeqAIJ,
3607: MatTransColoringApplyDenToSp_SeqAIJ,
3608: MatRARtNumeric_SeqAIJ_SeqAIJ,
3609: NULL,
3610: NULL,
3611: /*129*/ MatFDColoringSetUp_SeqXAIJ,
3612: MatFindOffBlockDiagonalEntries_SeqAIJ,
3613: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3614: MatDestroySubMatrices_SeqAIJ,
3615: NULL,
3616: /*134*/ NULL,
3617: MatCreateGraph_Simple_AIJ,
3618: MatTransposeSymbolic_SeqAIJ,
3619: MatEliminateZeros_SeqAIJ,
3620: MatGetRowSumAbs_SeqAIJ,
3621: /*139*/ NULL,
3622: NULL,
3623: NULL,
3624: MatCopyHashToXAIJ_Seq_Hash,
3625: NULL};
3627: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3628: {
3629: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3630: PetscInt i, nz, n;
3632: PetscFunctionBegin;
3633: nz = aij->maxnz;
3634: n = mat->rmap->n;
3635: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3636: aij->nz = nz;
3637: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3638: PetscFunctionReturn(PETSC_SUCCESS);
3639: }
3641: /*
3642: * Given a sparse matrix with global column indices, compact it by using a local column space.
3643: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3644: */
3645: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3646: {
3647: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3648: PetscHMapI gid1_lid1;
3649: PetscHashIter tpos;
3650: PetscInt gid, lid, i, ec, nz = aij->nz;
3651: PetscInt *garray, *jj = aij->j;
3653: PetscFunctionBegin;
3655: PetscAssertPointer(mapping, 2);
3656: /* use a table */
3657: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3658: ec = 0;
3659: for (i = 0; i < nz; i++) {
3660: PetscInt data, gid1 = jj[i] + 1;
3661: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3662: if (!data) {
3663: /* one based table */
3664: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3665: }
3666: }
3667: /* form array of columns we need */
3668: PetscCall(PetscMalloc1(ec, &garray));
3669: PetscHashIterBegin(gid1_lid1, tpos);
3670: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3671: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3672: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3673: PetscHashIterNext(gid1_lid1, tpos);
3674: gid--;
3675: lid--;
3676: garray[lid] = gid;
3677: }
3678: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3679: PetscCall(PetscHMapIClear(gid1_lid1));
3680: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3681: /* compact out the extra columns in B */
3682: for (i = 0; i < nz; i++) {
3683: PetscInt gid1 = jj[i] + 1;
3684: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3685: lid--;
3686: jj[i] = lid;
3687: }
3688: PetscCall(PetscLayoutDestroy(&mat->cmap));
3689: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3690: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3691: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3692: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3693: PetscFunctionReturn(PETSC_SUCCESS);
3694: }
3696: /*@
3697: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3698: in the matrix.
3700: Input Parameters:
3701: + mat - the `MATSEQAIJ` matrix
3702: - indices - the column indices
3704: Level: advanced
3706: Notes:
3707: This can be called if you have precomputed the nonzero structure of the
3708: matrix and want to provide it to the matrix object to improve the performance
3709: of the `MatSetValues()` operation.
3711: You MUST have set the correct numbers of nonzeros per row in the call to
3712: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3714: MUST be called before any calls to `MatSetValues()`
3716: The indices should start with zero, not one.
3718: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3719: @*/
3720: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3721: {
3722: PetscFunctionBegin;
3724: PetscAssertPointer(indices, 2);
3725: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3726: PetscFunctionReturn(PETSC_SUCCESS);
3727: }
3729: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3730: {
3731: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3732: size_t nz = aij->i[mat->rmap->n];
3734: PetscFunctionBegin;
3735: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3737: /* allocate space for values if not already there */
3738: if (!aij->saved_values) PetscCall(PetscMalloc1(nz + 1, &aij->saved_values));
3740: /* copy values over */
3741: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3742: PetscFunctionReturn(PETSC_SUCCESS);
3743: }
3745: /*@
3746: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3747: nonlinear portion.
3749: Logically Collect
3751: Input Parameter:
3752: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3754: Level: advanced
3756: Example Usage:
3757: .vb
3758: Using SNES
3759: Create Jacobian matrix
3760: Set linear terms into matrix
3761: Apply boundary conditions to matrix, at this time matrix must have
3762: final nonzero structure (i.e. setting the nonlinear terms and applying
3763: boundary conditions again will not change the nonzero structure
3764: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3765: MatStoreValues(mat);
3766: Call SNESSetJacobian() with matrix
3767: In your Jacobian routine
3768: MatRetrieveValues(mat);
3769: Set nonlinear terms in matrix
3771: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3772: // build linear portion of Jacobian
3773: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3774: MatStoreValues(mat);
3775: loop over nonlinear iterations
3776: MatRetrieveValues(mat);
3777: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3778: // call MatAssemblyBegin/End() on matrix
3779: Solve linear system with Jacobian
3780: endloop
3781: .ve
3783: Notes:
3784: Matrix must already be assembled before calling this routine
3785: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3786: calling this routine.
3788: When this is called multiple times it overwrites the previous set of stored values
3789: and does not allocated additional space.
3791: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3792: @*/
3793: PetscErrorCode MatStoreValues(Mat mat)
3794: {
3795: PetscFunctionBegin;
3797: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3798: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3799: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3800: PetscFunctionReturn(PETSC_SUCCESS);
3801: }
3803: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3804: {
3805: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3806: PetscInt nz = aij->i[mat->rmap->n];
3808: PetscFunctionBegin;
3809: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3810: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3811: /* copy values over */
3812: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3813: PetscFunctionReturn(PETSC_SUCCESS);
3814: }
3816: /*@
3817: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3819: Logically Collect
3821: Input Parameter:
3822: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3824: Level: advanced
3826: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3827: @*/
3828: PetscErrorCode MatRetrieveValues(Mat mat)
3829: {
3830: PetscFunctionBegin;
3832: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3833: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3834: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3835: PetscFunctionReturn(PETSC_SUCCESS);
3836: }
3838: /*@
3839: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3840: (the default parallel PETSc format). For good matrix assembly performance
3841: the user should preallocate the matrix storage by setting the parameter `nz`
3842: (or the array `nnz`).
3844: Collective
3846: Input Parameters:
3847: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3848: . m - number of rows
3849: . n - number of columns
3850: . nz - number of nonzeros per row (same for all rows)
3851: - nnz - array containing the number of nonzeros in the various rows
3852: (possibly different for each row) or NULL
3854: Output Parameter:
3855: . A - the matrix
3857: Options Database Keys:
3858: + -mat_no_inode - Do not use inodes
3859: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3861: Level: intermediate
3863: Notes:
3864: It is recommend to use `MatCreateFromOptions()` instead of this routine
3866: If `nnz` is given then `nz` is ignored
3868: The `MATSEQAIJ` format, also called
3869: compressed row storage, is fully compatible with standard Fortran
3870: storage. That is, the stored row and column indices can begin at
3871: either one (as in Fortran) or zero.
3873: Specify the preallocated storage with either `nz` or `nnz` (not both).
3874: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3875: allocation.
3877: By default, this format uses inodes (identical nodes) when possible, to
3878: improve numerical efficiency of matrix-vector products and solves. We
3879: search for consecutive rows with the same nonzero structure, thereby
3880: reusing matrix information to achieve increased efficiency.
3882: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3883: @*/
3884: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3885: {
3886: PetscFunctionBegin;
3887: PetscCall(MatCreate(comm, A));
3888: PetscCall(MatSetSizes(*A, m, n, m, n));
3889: PetscCall(MatSetType(*A, MATSEQAIJ));
3890: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3891: PetscFunctionReturn(PETSC_SUCCESS);
3892: }
3894: /*@
3895: MatSeqAIJSetPreallocation - For good matrix assembly performance
3896: the user should preallocate the matrix storage by setting the parameter nz
3897: (or the array nnz). By setting these parameters accurately, performance
3898: during matrix assembly can be increased by more than a factor of 50.
3900: Collective
3902: Input Parameters:
3903: + B - The matrix
3904: . nz - number of nonzeros per row (same for all rows)
3905: - nnz - array containing the number of nonzeros in the various rows
3906: (possibly different for each row) or NULL
3908: Options Database Keys:
3909: + -mat_no_inode - Do not use inodes
3910: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3912: Level: intermediate
3914: Notes:
3915: If `nnz` is given then `nz` is ignored
3917: The `MATSEQAIJ` format also called
3918: compressed row storage, is fully compatible with standard Fortran
3919: storage. That is, the stored row and column indices can begin at
3920: either one (as in Fortran) or zero. See the users' manual for details.
3922: Specify the preallocated storage with either `nz` or `nnz` (not both).
3923: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3924: allocation.
3926: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3927: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3928: You can also run with the option -info and look for messages with the string
3929: malloc in them to see if additional memory allocation was needed.
3931: Developer Notes:
3932: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3933: entries or columns indices
3935: By default, this format uses inodes (identical nodes) when possible, to
3936: improve numerical efficiency of matrix-vector products and solves. We
3937: search for consecutive rows with the same nonzero structure, thereby
3938: reusing matrix information to achieve increased efficiency.
3940: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3941: `MatSeqAIJSetTotalPreallocation()`
3942: @*/
3943: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3944: {
3945: PetscFunctionBegin;
3948: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3949: PetscFunctionReturn(PETSC_SUCCESS);
3950: }
3952: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3953: {
3954: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3955: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3956: PetscInt i;
3958: PetscFunctionBegin;
3959: if (B->hash_active) {
3960: B->ops[0] = b->cops;
3961: PetscCall(PetscHMapIJVDestroy(&b->ht));
3962: PetscCall(PetscFree(b->dnz));
3963: B->hash_active = PETSC_FALSE;
3964: }
3965: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3966: if (nz == MAT_SKIP_ALLOCATION) {
3967: skipallocation = PETSC_TRUE;
3968: nz = 0;
3969: }
3970: PetscCall(PetscLayoutSetUp(B->rmap));
3971: PetscCall(PetscLayoutSetUp(B->cmap));
3973: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3974: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3975: if (nnz) {
3976: for (i = 0; i < B->rmap->n; i++) {
3977: 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]);
3978: 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);
3979: }
3980: }
3982: B->preallocated = PETSC_TRUE;
3983: if (!skipallocation) {
3984: if (!b->imax) PetscCall(PetscMalloc1(B->rmap->n, &b->imax));
3985: if (!b->ilen) {
3986: /* b->ilen will count nonzeros in each row so far. */
3987: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
3988: } else {
3989: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
3990: }
3991: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
3992: if (!nnz) {
3993: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3994: else if (nz < 0) nz = 1;
3995: nz = PetscMin(nz, B->cmap->n);
3996: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
3997: PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
3998: } else {
3999: PetscInt64 nz64 = 0;
4000: for (i = 0; i < B->rmap->n; i++) {
4001: b->imax[i] = nnz[i];
4002: nz64 += nnz[i];
4003: }
4004: PetscCall(PetscIntCast(nz64, &nz));
4005: }
4007: /* allocate the matrix space */
4008: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4009: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
4010: PetscCall(PetscShmgetAllocateArray(B->rmap->n + 1, sizeof(PetscInt), (void **)&b->i));
4011: b->free_ij = PETSC_TRUE;
4012: if (B->structure_only) {
4013: b->free_a = PETSC_FALSE;
4014: } else {
4015: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)&b->a));
4016: b->free_a = PETSC_TRUE;
4017: }
4018: b->i[0] = 0;
4019: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4020: } else {
4021: b->free_a = PETSC_FALSE;
4022: b->free_ij = PETSC_FALSE;
4023: }
4025: if (b->ipre && nnz != b->ipre && b->imax) {
4026: /* reserve user-requested sparsity */
4027: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4028: }
4030: b->nz = 0;
4031: b->maxnz = nz;
4032: B->info.nz_unneeded = (double)b->maxnz;
4033: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4034: B->was_assembled = PETSC_FALSE;
4035: B->assembled = PETSC_FALSE;
4036: /* We simply deem preallocation has changed nonzero state. Updating the state
4037: will give clients (like AIJKokkos) a chance to know something has happened.
4038: */
4039: B->nonzerostate++;
4040: PetscFunctionReturn(PETSC_SUCCESS);
4041: }
4043: PetscErrorCode MatResetPreallocation_SeqAIJ_Private(Mat A, PetscBool *memoryreset)
4044: {
4045: Mat_SeqAIJ *a;
4046: PetscInt i;
4047: PetscBool skipreset;
4049: PetscFunctionBegin;
4052: PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
4053: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
4055: /* Check local size. If zero, then return */
4056: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
4058: a = (Mat_SeqAIJ *)A->data;
4059: /* if no saved info, we error out */
4060: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
4062: PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and cannot reset preallocation ");
4064: PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4065: if (skipreset) PetscCall(MatZeroEntries(A));
4066: else {
4067: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4068: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4069: a->i[0] = 0;
4070: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4071: A->preallocated = PETSC_TRUE;
4072: a->nz = 0;
4073: a->maxnz = a->i[A->rmap->n];
4074: A->info.nz_unneeded = (double)a->maxnz;
4075: A->was_assembled = PETSC_FALSE;
4076: A->assembled = PETSC_FALSE;
4077: A->nonzerostate++;
4078: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
4079: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4080: }
4081: if (memoryreset) *memoryreset = (PetscBool)!skipreset;
4082: PetscFunctionReturn(PETSC_SUCCESS);
4083: }
4085: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4086: {
4087: PetscFunctionBegin;
4088: PetscCall(MatResetPreallocation_SeqAIJ_Private(A, NULL));
4089: PetscFunctionReturn(PETSC_SUCCESS);
4090: }
4092: /*@
4093: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4095: Input Parameters:
4096: + B - the matrix
4097: . i - the indices into `j` for the start of each row (indices start with zero)
4098: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4099: - v - optional values in the matrix, use `NULL` if not provided
4101: Level: developer
4103: Notes:
4104: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4106: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4107: structure will be the union of all the previous nonzero structures.
4109: Developer Notes:
4110: 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
4111: then just copies the `v` values directly with `PetscMemcpy()`.
4113: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4115: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4116: @*/
4117: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4118: {
4119: PetscFunctionBegin;
4122: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4123: PetscFunctionReturn(PETSC_SUCCESS);
4124: }
4126: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4127: {
4128: PetscInt i;
4129: PetscInt m, n;
4130: PetscInt nz;
4131: PetscInt *nnz;
4133: PetscFunctionBegin;
4134: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4136: PetscCall(PetscLayoutSetUp(B->rmap));
4137: PetscCall(PetscLayoutSetUp(B->cmap));
4139: PetscCall(MatGetSize(B, &m, &n));
4140: PetscCall(PetscMalloc1(m + 1, &nnz));
4141: for (i = 0; i < m; i++) {
4142: nz = Ii[i + 1] - Ii[i];
4143: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4144: nnz[i] = nz;
4145: }
4146: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4147: PetscCall(PetscFree(nnz));
4149: 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));
4151: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4152: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4154: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4155: PetscFunctionReturn(PETSC_SUCCESS);
4156: }
4158: /*@
4159: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4161: Input Parameters:
4162: + A - left-hand side matrix
4163: . B - right-hand side matrix
4164: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4166: Output Parameter:
4167: . C - Kronecker product of `A` and `B`
4169: Level: intermediate
4171: Note:
4172: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4174: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4175: @*/
4176: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4177: {
4178: PetscFunctionBegin;
4183: PetscAssertPointer(C, 4);
4184: if (reuse == MAT_REUSE_MATRIX) {
4187: }
4188: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4189: PetscFunctionReturn(PETSC_SUCCESS);
4190: }
4192: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4193: {
4194: Mat newmat;
4195: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4196: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4197: PetscScalar *v;
4198: const PetscScalar *aa, *ba;
4199: 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;
4200: PetscBool flg;
4202: PetscFunctionBegin;
4203: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4204: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4205: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4206: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4207: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4208: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4209: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4210: if (reuse == MAT_INITIAL_MATRIX) {
4211: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4212: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4213: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4214: PetscCall(MatSetType(newmat, MATAIJ));
4215: i[0] = 0;
4216: for (m = 0; m < am; ++m) {
4217: for (p = 0; p < bm; ++p) {
4218: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4219: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4220: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4221: }
4222: }
4223: }
4224: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4225: *C = newmat;
4226: PetscCall(PetscFree2(i, j));
4227: nnz = 0;
4228: }
4229: PetscCall(MatSeqAIJGetArray(*C, &v));
4230: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4231: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4232: for (m = 0; m < am; ++m) {
4233: for (p = 0; p < bm; ++p) {
4234: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4235: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4236: }
4237: }
4238: }
4239: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4240: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4241: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4242: PetscFunctionReturn(PETSC_SUCCESS);
4243: }
4245: #include <../src/mat/impls/dense/seq/dense.h>
4246: #include <petsc/private/kernels/petscaxpy.h>
4248: /*
4249: Computes (B'*A')' since computing B*A directly is untenable
4251: n p p
4252: [ ] [ ] [ ]
4253: m [ A ] * n [ B ] = m [ C ]
4254: [ ] [ ] [ ]
4256: */
4257: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4258: {
4259: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4260: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4261: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4262: PetscInt i, j, n, m, q, p;
4263: const PetscInt *ii, *idx;
4264: const PetscScalar *b, *a, *a_q;
4265: PetscScalar *c, *c_q;
4266: PetscInt clda = sub_c->lda;
4267: PetscInt alda = sub_a->lda;
4269: PetscFunctionBegin;
4270: m = A->rmap->n;
4271: n = A->cmap->n;
4272: p = B->cmap->n;
4273: a = sub_a->v;
4274: b = sub_b->a;
4275: c = sub_c->v;
4276: if (clda == m) {
4277: PetscCall(PetscArrayzero(c, m * p));
4278: } else {
4279: for (j = 0; j < p; j++)
4280: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4281: }
4282: ii = sub_b->i;
4283: idx = sub_b->j;
4284: for (i = 0; i < n; i++) {
4285: q = ii[i + 1] - ii[i];
4286: while (q-- > 0) {
4287: c_q = c + clda * (*idx);
4288: a_q = a + alda * i;
4289: PetscKernelAXPY(c_q, *b, a_q, m);
4290: idx++;
4291: b++;
4292: }
4293: }
4294: PetscFunctionReturn(PETSC_SUCCESS);
4295: }
4297: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4298: {
4299: PetscInt m = A->rmap->n, n = B->cmap->n;
4300: PetscBool cisdense;
4302: PetscFunctionBegin;
4303: 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);
4304: PetscCall(MatSetSizes(C, m, n, m, n));
4305: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4306: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4307: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4308: PetscCall(MatSetUp(C));
4310: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4311: PetscFunctionReturn(PETSC_SUCCESS);
4312: }
4314: /*MC
4315: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4316: based on compressed sparse row format.
4318: Options Database Key:
4319: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4321: Level: beginner
4323: Notes:
4324: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4325: in this case the values associated with the rows and columns one passes in are set to zero
4326: in the matrix
4328: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4329: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4331: Developer Note:
4332: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4334: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4335: M*/
4337: /*MC
4338: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4340: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4341: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4342: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4343: for communicators controlling multiple processes. It is recommended that you call both of
4344: the above preallocation routines for simplicity.
4346: Options Database Key:
4347: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4349: Level: beginner
4351: Note:
4352: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4353: enough exist.
4355: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4356: M*/
4358: /*MC
4359: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4361: Options Database Key:
4362: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4364: Level: beginner
4366: Note:
4367: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4368: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4369: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4370: for communicators controlling multiple processes. It is recommended that you call both of
4371: the above preallocation routines for simplicity.
4373: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4374: M*/
4376: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4377: #if defined(PETSC_HAVE_ELEMENTAL)
4378: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4379: #endif
4380: #if defined(PETSC_HAVE_SCALAPACK)
4381: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4382: #endif
4383: #if defined(PETSC_HAVE_HYPRE)
4384: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4385: #endif
4387: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4388: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4389: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4391: /*@C
4392: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4394: Not Collective
4396: Input Parameter:
4397: . A - a `MATSEQAIJ` matrix
4399: Output Parameter:
4400: . array - pointer to the data
4402: Level: intermediate
4404: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`
4405: @*/
4406: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar *array[])
4407: {
4408: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4410: PetscFunctionBegin;
4411: if (aij->ops->getarray) {
4412: PetscCall((*aij->ops->getarray)(A, array));
4413: } else {
4414: *array = aij->a;
4415: }
4416: PetscFunctionReturn(PETSC_SUCCESS);
4417: }
4419: /*@C
4420: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4422: Not Collective
4424: Input Parameters:
4425: + A - a `MATSEQAIJ` matrix
4426: - array - pointer to the data
4428: Level: intermediate
4430: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`
4431: @*/
4432: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar *array[])
4433: {
4434: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4436: PetscFunctionBegin;
4437: if (aij->ops->restorearray) {
4438: PetscCall((*aij->ops->restorearray)(A, array));
4439: } else {
4440: *array = NULL;
4441: }
4442: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4443: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4444: PetscFunctionReturn(PETSC_SUCCESS);
4445: }
4447: /*@C
4448: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4450: Not Collective; No Fortran Support
4452: Input Parameter:
4453: . A - a `MATSEQAIJ` matrix
4455: Output Parameter:
4456: . array - pointer to the data
4458: Level: intermediate
4460: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4461: @*/
4462: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar *array[])
4463: {
4464: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4466: PetscFunctionBegin;
4467: if (aij->ops->getarrayread) {
4468: PetscCall((*aij->ops->getarrayread)(A, array));
4469: } else {
4470: *array = aij->a;
4471: }
4472: PetscFunctionReturn(PETSC_SUCCESS);
4473: }
4475: /*@C
4476: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4478: Not Collective; No Fortran Support
4480: Input Parameter:
4481: . A - a `MATSEQAIJ` matrix
4483: Output Parameter:
4484: . array - pointer to the data
4486: Level: intermediate
4488: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4489: @*/
4490: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar *array[])
4491: {
4492: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4494: PetscFunctionBegin;
4495: if (aij->ops->restorearrayread) {
4496: PetscCall((*aij->ops->restorearrayread)(A, array));
4497: } else {
4498: *array = NULL;
4499: }
4500: PetscFunctionReturn(PETSC_SUCCESS);
4501: }
4503: /*@C
4504: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4506: Not Collective; No Fortran Support
4508: Input Parameter:
4509: . A - a `MATSEQAIJ` matrix
4511: Output Parameter:
4512: . array - pointer to the data
4514: Level: intermediate
4516: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4517: @*/
4518: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar *array[])
4519: {
4520: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4522: PetscFunctionBegin;
4523: if (aij->ops->getarraywrite) {
4524: PetscCall((*aij->ops->getarraywrite)(A, array));
4525: } else {
4526: *array = aij->a;
4527: }
4528: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4529: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4530: PetscFunctionReturn(PETSC_SUCCESS);
4531: }
4533: /*@C
4534: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4536: Not Collective; No Fortran Support
4538: Input Parameter:
4539: . A - a MATSEQAIJ matrix
4541: Output Parameter:
4542: . array - pointer to the data
4544: Level: intermediate
4546: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4547: @*/
4548: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar *array[])
4549: {
4550: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4552: PetscFunctionBegin;
4553: if (aij->ops->restorearraywrite) {
4554: PetscCall((*aij->ops->restorearraywrite)(A, array));
4555: } else {
4556: *array = NULL;
4557: }
4558: PetscFunctionReturn(PETSC_SUCCESS);
4559: }
4561: /*@C
4562: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4564: Not Collective; No Fortran Support
4566: Input Parameter:
4567: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4569: Output Parameters:
4570: + i - row map array of the matrix
4571: . j - column index array of the matrix
4572: . a - data array of the matrix
4573: - mtype - memory type of the arrays
4575: Level: developer
4577: Notes:
4578: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4579: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4581: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4582: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4584: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4585: @*/
4586: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt *i[], const PetscInt *j[], PetscScalar *a[], PetscMemType *mtype)
4587: {
4588: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4590: PetscFunctionBegin;
4591: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4592: if (aij->ops->getcsrandmemtype) {
4593: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4594: } else {
4595: if (i) *i = aij->i;
4596: if (j) *j = aij->j;
4597: if (a) *a = aij->a;
4598: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4599: }
4600: PetscFunctionReturn(PETSC_SUCCESS);
4601: }
4603: /*@
4604: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4606: Not Collective
4608: Input Parameter:
4609: . A - a `MATSEQAIJ` matrix
4611: Output Parameter:
4612: . nz - the maximum number of nonzeros in any row
4614: Level: intermediate
4616: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`
4617: @*/
4618: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4619: {
4620: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4622: PetscFunctionBegin;
4623: *nz = aij->rmax;
4624: PetscFunctionReturn(PETSC_SUCCESS);
4625: }
4627: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void **data)
4628: {
4629: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)*data;
4631: PetscFunctionBegin;
4632: PetscCall(PetscFree(coo->perm));
4633: PetscCall(PetscFree(coo->jmap));
4634: PetscCall(PetscFree(coo));
4635: PetscFunctionReturn(PETSC_SUCCESS);
4636: }
4638: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4639: {
4640: MPI_Comm comm;
4641: PetscInt *i, *j;
4642: PetscInt M, N, row, iprev;
4643: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4644: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4645: PetscInt *Aj;
4646: PetscScalar *Aa;
4647: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)mat->data;
4648: MatType rtype;
4649: PetscCount *perm, *jmap;
4650: MatCOOStruct_SeqAIJ *coo;
4651: PetscBool isorted;
4652: PetscBool hypre;
4654: PetscFunctionBegin;
4655: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4656: PetscCall(MatGetSize(mat, &M, &N));
4657: i = coo_i;
4658: j = coo_j;
4659: PetscCall(PetscMalloc1(coo_n, &perm));
4661: /* 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) */
4662: isorted = PETSC_TRUE;
4663: iprev = PETSC_INT_MIN;
4664: for (k = 0; k < coo_n; k++) {
4665: if (j[k] < 0) i[k] = -1;
4666: if (isorted) {
4667: if (i[k] < iprev) isorted = PETSC_FALSE;
4668: else iprev = i[k];
4669: }
4670: perm[k] = k;
4671: }
4673: /* Sort by row if not already */
4674: if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4675: PetscCheck(i == NULL || i[coo_n - 1] < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "COO row index %" PetscInt_FMT " is >= the matrix row size %" PetscInt_FMT, i[coo_n - 1], M);
4677: /* Advance k to the first row with a non-negative index */
4678: for (k = 0; k < coo_n; k++)
4679: if (i[k] >= 0) break;
4680: nneg = k;
4681: 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 */
4682: nnz = 0; /* Total number of unique nonzeros to be counted */
4683: jmap++; /* Inc jmap by 1 for convenience */
4685: PetscCall(PetscShmgetAllocateArray(M + 1, sizeof(PetscInt), (void **)&Ai)); /* CSR of A */
4686: PetscCall(PetscArrayzero(Ai, M + 1));
4687: PetscCall(PetscShmgetAllocateArray(coo_n - nneg, sizeof(PetscInt), (void **)&Aj)); /* We have at most coo_n-nneg unique nonzeros */
4689: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
4691: /* In each row, sort by column, then unique column indices to get row length */
4692: Ai++; /* Inc by 1 for convenience */
4693: q = 0; /* q-th unique nonzero, with q starting from 0 */
4694: while (k < coo_n) {
4695: PetscBool strictly_sorted; // this row is strictly sorted?
4696: PetscInt jprev;
4698: /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4699: row = i[k];
4700: start = k;
4701: jprev = PETSC_INT_MIN;
4702: strictly_sorted = PETSC_TRUE;
4703: while (k < coo_n && i[k] == row) {
4704: if (strictly_sorted) {
4705: if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4706: else jprev = j[k];
4707: }
4708: k++;
4709: }
4710: end = k;
4712: /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4713: if (hypre) {
4714: PetscInt minj = PETSC_INT_MAX;
4715: PetscBool hasdiag = PETSC_FALSE;
4717: if (strictly_sorted) { // fast path to swap the first and the diag
4718: PetscCount tmp;
4719: for (p = start; p < end; p++) {
4720: if (j[p] == row && p != start) {
4721: j[p] = j[start]; // swap j[], so that the diagonal value will go first (manipulated by perm[])
4722: j[start] = row;
4723: tmp = perm[start];
4724: perm[start] = perm[p]; // also swap perm[] so we can save the call to PetscSortIntWithCountArray() below
4725: perm[p] = tmp;
4726: break;
4727: }
4728: }
4729: } else {
4730: for (p = start; p < end; p++) {
4731: hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4732: minj = PetscMin(minj, j[p]);
4733: }
4735: if (hasdiag) {
4736: for (p = start; p < end; p++) {
4737: if (j[p] == minj) j[p] = row;
4738: else if (j[p] == row) j[p] = minj;
4739: }
4740: }
4741: }
4742: }
4743: // sort by columns in a row. perm[] indicates their original order
4744: if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4745: PetscCheck(end == start || j[end - 1] < N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "COO column index %" PetscInt_FMT " is >= the matrix column size %" PetscInt_FMT, j[end - 1], N);
4747: if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4748: for (p = start; p < end; p++, q++) {
4749: Aj[q] = j[p];
4750: jmap[q] = 1;
4751: }
4752: PetscCall(PetscIntCast(end - start, Ai + row));
4753: nnz += Ai[row]; // q is already advanced
4754: } else {
4755: /* Find number of unique col entries in this row */
4756: Aj[q] = j[start]; /* Log the first nonzero in this row */
4757: jmap[q] = 1; /* Number of repeats of this nonzero entry */
4758: Ai[row] = 1;
4759: nnz++;
4761: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4762: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4763: q++;
4764: jmap[q] = 1;
4765: Aj[q] = j[p];
4766: Ai[row]++;
4767: nnz++;
4768: } else {
4769: jmap[q]++;
4770: }
4771: }
4772: q++; /* Move to next row and thus next unique nonzero */
4773: }
4774: }
4776: Ai--; /* Back to the beginning of Ai[] */
4777: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4778: jmap--; // Back to the beginning of jmap[]
4779: jmap[0] = 0;
4780: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4782: if (nnz < coo_n - nneg) { /* Reallocate with actual number of unique nonzeros */
4783: PetscCount *jmap_new;
4784: PetscInt *Aj_new;
4786: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4787: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4788: PetscCall(PetscFree(jmap));
4789: jmap = jmap_new;
4791: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscInt), (void **)&Aj_new));
4792: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4793: PetscCall(PetscShmgetDeallocateArray((void **)&Aj));
4794: Aj = Aj_new;
4795: }
4797: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4798: PetscCount *perm_new;
4800: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4801: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4802: PetscCall(PetscFree(perm));
4803: perm = perm_new;
4804: }
4806: PetscCall(MatGetRootType_Private(mat, &rtype));
4807: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscScalar), (void **)&Aa));
4808: PetscCall(PetscArrayzero(Aa, nnz));
4809: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4811: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4813: // Put the COO struct in a container and then attach that to the matrix
4814: PetscCall(PetscMalloc1(1, &coo));
4815: PetscCall(PetscIntCast(nnz, &coo->nz));
4816: coo->n = coo_n;
4817: coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4818: coo->jmap = jmap; // of length nnz+1
4819: coo->perm = perm;
4820: PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", coo, MatCOOStructDestroy_SeqAIJ));
4821: PetscFunctionReturn(PETSC_SUCCESS);
4822: }
4824: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4825: {
4826: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4827: PetscCount i, j, Annz = aseq->nz;
4828: PetscCount *perm, *jmap;
4829: PetscScalar *Aa;
4830: PetscContainer container;
4831: MatCOOStruct_SeqAIJ *coo;
4833: PetscFunctionBegin;
4834: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4835: PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4836: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4837: perm = coo->perm;
4838: jmap = coo->jmap;
4839: PetscCall(MatSeqAIJGetArray(A, &Aa));
4840: for (i = 0; i < Annz; i++) {
4841: PetscScalar sum = 0.0;
4842: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4843: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4844: }
4845: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4846: PetscFunctionReturn(PETSC_SUCCESS);
4847: }
4849: #if defined(PETSC_HAVE_CUDA)
4850: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4851: #endif
4852: #if defined(PETSC_HAVE_HIP)
4853: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4854: #endif
4855: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4856: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4857: #endif
4859: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4860: {
4861: Mat_SeqAIJ *b;
4862: PetscMPIInt size;
4864: PetscFunctionBegin;
4865: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4866: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4868: PetscCall(PetscNew(&b));
4870: B->data = (void *)b;
4871: B->ops[0] = MatOps_Values;
4872: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4874: b->row = NULL;
4875: b->col = NULL;
4876: b->icol = NULL;
4877: b->reallocs = 0;
4878: b->ignorezeroentries = PETSC_FALSE;
4879: b->roworiented = PETSC_TRUE;
4880: b->nonew = 0;
4881: b->diag = NULL;
4882: b->solve_work = NULL;
4883: B->spptr = NULL;
4884: b->saved_values = NULL;
4885: b->idiag = NULL;
4886: b->mdiag = NULL;
4887: b->ssor_work = NULL;
4888: b->omega = 1.0;
4889: b->fshift = 0.0;
4890: b->idiagvalid = PETSC_FALSE;
4891: b->ibdiagvalid = PETSC_FALSE;
4892: b->keepnonzeropattern = PETSC_FALSE;
4894: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4895: #if defined(PETSC_HAVE_MATLAB)
4896: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4897: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4898: #endif
4899: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4900: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4901: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4902: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4903: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4906: #if defined(PETSC_HAVE_MKL_SPARSE)
4907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4908: #endif
4909: #if defined(PETSC_HAVE_CUDA)
4910: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4911: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4913: #endif
4914: #if defined(PETSC_HAVE_HIP)
4915: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4918: #endif
4919: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4920: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4921: #endif
4922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4923: #if defined(PETSC_HAVE_ELEMENTAL)
4924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4925: #endif
4926: #if defined(PETSC_HAVE_SCALAPACK)
4927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4928: #endif
4929: #if defined(PETSC_HAVE_HYPRE)
4930: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4931: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4932: #endif
4933: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4940: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_SeqAIJ));
4941: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4942: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4943: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4944: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4945: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4946: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4949: PetscCall(MatCreate_SeqAIJ_Inode(B));
4950: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4951: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4952: PetscFunctionReturn(PETSC_SUCCESS);
4953: }
4955: /*
4956: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4957: */
4958: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4959: {
4960: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4961: PetscInt m = A->rmap->n, i;
4963: PetscFunctionBegin;
4964: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4966: C->factortype = A->factortype;
4967: c->row = NULL;
4968: c->col = NULL;
4969: c->icol = NULL;
4970: c->reallocs = 0;
4971: c->diagonaldense = a->diagonaldense;
4973: C->assembled = A->assembled;
4975: if (A->preallocated) {
4976: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4977: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4979: if (!A->hash_active) {
4980: PetscCall(PetscMalloc1(m, &c->imax));
4981: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4982: PetscCall(PetscMalloc1(m, &c->ilen));
4983: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
4985: /* allocate the matrix space */
4986: if (mallocmatspace) {
4987: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscScalar), (void **)&c->a));
4988: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscInt), (void **)&c->j));
4989: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4990: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
4991: c->free_a = PETSC_TRUE;
4992: c->free_ij = PETSC_TRUE;
4993: if (m > 0) {
4994: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
4995: if (cpvalues == MAT_COPY_VALUES) {
4996: const PetscScalar *aa;
4998: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4999: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5000: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5001: } else {
5002: PetscCall(PetscArrayzero(c->a, a->i[m]));
5003: }
5004: }
5005: }
5006: C->preallocated = PETSC_TRUE;
5007: } else {
5008: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5009: PetscCall(MatSetUp(C));
5010: }
5012: c->ignorezeroentries = a->ignorezeroentries;
5013: c->roworiented = a->roworiented;
5014: c->nonew = a->nonew;
5015: if (a->diag) {
5016: PetscCall(PetscMalloc1(m + 1, &c->diag));
5017: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5018: } else c->diag = NULL;
5020: c->solve_work = NULL;
5021: c->saved_values = NULL;
5022: c->idiag = NULL;
5023: c->ssor_work = NULL;
5024: c->keepnonzeropattern = a->keepnonzeropattern;
5026: c->rmax = a->rmax;
5027: c->nz = a->nz;
5028: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
5030: c->compressedrow.use = a->compressedrow.use;
5031: c->compressedrow.nrows = a->compressedrow.nrows;
5032: if (a->compressedrow.use) {
5033: i = a->compressedrow.nrows;
5034: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5035: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5036: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5037: } else {
5038: c->compressedrow.use = PETSC_FALSE;
5039: c->compressedrow.i = NULL;
5040: c->compressedrow.rindex = NULL;
5041: }
5042: c->nonzerorowcnt = a->nonzerorowcnt;
5043: C->nonzerostate = A->nonzerostate;
5045: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5046: }
5047: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5048: PetscFunctionReturn(PETSC_SUCCESS);
5049: }
5051: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5052: {
5053: PetscFunctionBegin;
5054: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5055: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5056: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5057: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5058: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5059: PetscFunctionReturn(PETSC_SUCCESS);
5060: }
5062: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5063: {
5064: PetscBool isbinary, ishdf5;
5066: PetscFunctionBegin;
5069: /* force binary viewer to load .info file if it has not yet done so */
5070: PetscCall(PetscViewerSetUp(viewer));
5071: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5072: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5073: if (isbinary) {
5074: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5075: } else if (ishdf5) {
5076: #if defined(PETSC_HAVE_HDF5)
5077: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5078: #else
5079: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5080: #endif
5081: } else {
5082: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
5083: }
5084: PetscFunctionReturn(PETSC_SUCCESS);
5085: }
5087: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5088: {
5089: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5090: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
5092: PetscFunctionBegin;
5093: PetscCall(PetscViewerSetUp(viewer));
5095: /* read in matrix header */
5096: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5097: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5098: M = header[1];
5099: N = header[2];
5100: nz = header[3];
5101: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5102: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5103: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5105: /* set block sizes from the viewer's .info file */
5106: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5107: /* set local and global sizes if not set already */
5108: if (mat->rmap->n < 0) mat->rmap->n = M;
5109: if (mat->cmap->n < 0) mat->cmap->n = N;
5110: if (mat->rmap->N < 0) mat->rmap->N = M;
5111: if (mat->cmap->N < 0) mat->cmap->N = N;
5112: PetscCall(PetscLayoutSetUp(mat->rmap));
5113: PetscCall(PetscLayoutSetUp(mat->cmap));
5115: /* check if the matrix sizes are correct */
5116: PetscCall(MatGetSize(mat, &rows, &cols));
5117: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
5119: /* read in row lengths */
5120: PetscCall(PetscMalloc1(M, &rowlens));
5121: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5122: /* check if sum(rowlens) is same as nz */
5123: sum = 0;
5124: for (i = 0; i < M; i++) sum += rowlens[i];
5125: PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
5126: /* preallocate and check sizes */
5127: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5128: PetscCall(MatGetSize(mat, &rows, &cols));
5129: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
5130: /* store row lengths */
5131: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5132: PetscCall(PetscFree(rowlens));
5134: /* fill in "i" row pointers */
5135: a->i[0] = 0;
5136: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5137: /* read in "j" column indices */
5138: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5139: /* read in "a" nonzero values */
5140: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5142: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5143: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5144: PetscFunctionReturn(PETSC_SUCCESS);
5145: }
5147: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5148: {
5149: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5150: const PetscScalar *aa, *ba;
5151: #if defined(PETSC_USE_COMPLEX)
5152: PetscInt k;
5153: #endif
5155: PetscFunctionBegin;
5156: /* If the matrix dimensions are not equal,or no of nonzeros */
5157: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5158: *flg = PETSC_FALSE;
5159: PetscFunctionReturn(PETSC_SUCCESS);
5160: }
5162: /* if the a->i are the same */
5163: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5164: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5166: /* if a->j are the same */
5167: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5168: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5170: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5171: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5172: /* if a->a are the same */
5173: #if defined(PETSC_USE_COMPLEX)
5174: for (k = 0; k < a->nz; k++) {
5175: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5176: *flg = PETSC_FALSE;
5177: PetscFunctionReturn(PETSC_SUCCESS);
5178: }
5179: }
5180: #else
5181: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5182: #endif
5183: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5184: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5185: PetscFunctionReturn(PETSC_SUCCESS);
5186: }
5188: /*@
5189: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5190: provided by the user.
5192: Collective
5194: Input Parameters:
5195: + comm - must be an MPI communicator of size 1
5196: . m - number of rows
5197: . n - number of columns
5198: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5199: . j - column indices
5200: - a - matrix values
5202: Output Parameter:
5203: . mat - the matrix
5205: Level: intermediate
5207: Notes:
5208: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5209: once the matrix is destroyed and not before
5211: You cannot set new nonzero locations into this matrix, that will generate an error.
5213: The `i` and `j` indices are 0 based
5215: The format which is used for the sparse matrix input, is equivalent to a
5216: row-major ordering.. i.e for the following matrix, the input data expected is
5217: as shown
5218: .vb
5219: 1 0 0
5220: 2 0 3
5221: 4 5 6
5223: i = {0,1,3,6} [size = nrow+1 = 3+1]
5224: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5225: v = {1,2,3,4,5,6} [size = 6]
5226: .ve
5228: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5229: @*/
5230: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5231: {
5232: PetscInt ii;
5233: Mat_SeqAIJ *aij;
5234: PetscInt jj;
5236: PetscFunctionBegin;
5237: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5238: PetscCall(MatCreate(comm, mat));
5239: PetscCall(MatSetSizes(*mat, m, n, m, n));
5240: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5241: PetscCall(MatSetType(*mat, MATSEQAIJ));
5242: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5243: aij = (Mat_SeqAIJ *)(*mat)->data;
5244: PetscCall(PetscMalloc1(m, &aij->imax));
5245: PetscCall(PetscMalloc1(m, &aij->ilen));
5247: aij->i = i;
5248: aij->j = j;
5249: aij->a = a;
5250: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5251: aij->free_a = PETSC_FALSE;
5252: aij->free_ij = PETSC_FALSE;
5254: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5255: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5256: if (PetscDefined(USE_DEBUG)) {
5257: PetscCheck(i[ii + 1] - i[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, i[ii + 1] - i[ii]);
5258: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5259: PetscCheck(j[jj] >= j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", jj - i[ii], j[jj], ii);
5260: PetscCheck(j[jj] != j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", jj - i[ii], j[jj], ii);
5261: }
5262: }
5263: }
5264: if (PetscDefined(USE_DEBUG)) {
5265: for (ii = 0; ii < aij->i[m]; ii++) {
5266: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5267: PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT " last column = %" PetscInt_FMT, ii, j[ii], n - 1);
5268: }
5269: }
5271: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5272: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5273: PetscFunctionReturn(PETSC_SUCCESS);
5274: }
5276: /*@
5277: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5278: provided by the user.
5280: Collective
5282: Input Parameters:
5283: + comm - must be an MPI communicator of size 1
5284: . m - number of rows
5285: . n - number of columns
5286: . i - row indices
5287: . j - column indices
5288: . a - matrix values
5289: . nz - number of nonzeros
5290: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5292: Output Parameter:
5293: . mat - the matrix
5295: Level: intermediate
5297: Example:
5298: For the following matrix, the input data expected is as shown (using 0 based indexing)
5299: .vb
5300: 1 0 0
5301: 2 0 3
5302: 4 5 6
5304: i = {0,1,1,2,2,2}
5305: j = {0,0,2,0,1,2}
5306: v = {1,2,3,4,5,6}
5307: .ve
5309: Note:
5310: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5311: and are particularly useful in iterative applications.
5313: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5314: @*/
5315: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscCount nz, PetscBool idx)
5316: {
5317: PetscInt ii, *nnz, one = 1, row, col;
5319: PetscFunctionBegin;
5320: PetscCall(PetscCalloc1(m, &nnz));
5321: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5322: PetscCall(MatCreate(comm, mat));
5323: PetscCall(MatSetSizes(*mat, m, n, m, n));
5324: PetscCall(MatSetType(*mat, MATSEQAIJ));
5325: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5326: for (ii = 0; ii < nz; ii++) {
5327: if (idx) {
5328: row = i[ii] - 1;
5329: col = j[ii] - 1;
5330: } else {
5331: row = i[ii];
5332: col = j[ii];
5333: }
5334: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5335: }
5336: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5337: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5338: PetscCall(PetscFree(nnz));
5339: PetscFunctionReturn(PETSC_SUCCESS);
5340: }
5342: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5343: {
5344: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5346: PetscFunctionBegin;
5347: a->idiagvalid = PETSC_FALSE;
5348: a->ibdiagvalid = PETSC_FALSE;
5350: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5351: PetscFunctionReturn(PETSC_SUCCESS);
5352: }
5354: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5355: {
5356: PetscFunctionBegin;
5357: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5358: PetscFunctionReturn(PETSC_SUCCESS);
5359: }
5361: /*
5362: Permute A into C's *local* index space using rowemb,colemb.
5363: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5364: of [0,m), colemb is in [0,n).
5365: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5366: */
5367: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5368: {
5369: /* If making this function public, change the error returned in this function away from _PLIB. */
5370: Mat_SeqAIJ *Baij;
5371: PetscBool seqaij;
5372: PetscInt m, n, *nz, i, j, count;
5373: PetscScalar v;
5374: const PetscInt *rowindices, *colindices;
5376: PetscFunctionBegin;
5377: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5378: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5379: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5380: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5381: if (rowemb) {
5382: PetscCall(ISGetLocalSize(rowemb, &m));
5383: PetscCheck(m == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row IS of size %" PetscInt_FMT " is incompatible with matrix row size %" PetscInt_FMT, m, B->rmap->n);
5384: } else {
5385: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5386: }
5387: if (colemb) {
5388: PetscCall(ISGetLocalSize(colemb, &n));
5389: PetscCheck(n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Diag col IS of size %" PetscInt_FMT " is incompatible with input matrix col size %" PetscInt_FMT, n, B->cmap->n);
5390: } else {
5391: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5392: }
5394: Baij = (Mat_SeqAIJ *)B->data;
5395: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5396: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5397: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5398: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5399: PetscCall(PetscFree(nz));
5400: }
5401: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5402: count = 0;
5403: rowindices = NULL;
5404: colindices = NULL;
5405: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5406: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5407: for (i = 0; i < B->rmap->n; i++) {
5408: PetscInt row;
5409: row = i;
5410: if (rowindices) row = rowindices[i];
5411: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5412: PetscInt col;
5413: col = Baij->j[count];
5414: if (colindices) col = colindices[col];
5415: v = Baij->a[count];
5416: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5417: ++count;
5418: }
5419: }
5420: /* FIXME: set C's nonzerostate correctly. */
5421: /* Assembly for C is necessary. */
5422: C->preallocated = PETSC_TRUE;
5423: C->assembled = PETSC_TRUE;
5424: C->was_assembled = PETSC_FALSE;
5425: PetscFunctionReturn(PETSC_SUCCESS);
5426: }
5428: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5429: {
5430: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5431: MatScalar *aa = a->a;
5432: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5433: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5435: PetscFunctionBegin;
5436: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5437: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5438: for (i = 1; i <= m; i++) {
5439: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5440: for (k = ai[i - 1]; k < ai[i]; k++) {
5441: if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5442: else {
5443: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5444: aa[k - fshift] = aa[k];
5445: aj[k - fshift] = aj[k];
5446: }
5447: }
5448: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5449: fshift_prev = fshift;
5450: /* reset ilen and imax for each row */
5451: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5452: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5453: rmax = PetscMax(rmax, ailen[i - 1]);
5454: }
5455: if (fshift) {
5456: if (m) {
5457: ai[m] -= fshift;
5458: a->nz = ai[m];
5459: }
5460: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; zeros eliminated: %" PetscInt_FMT "; nonzeros left: %" PetscInt_FMT "\n", m, A->cmap->n, fshift, a->nz));
5461: A->nonzerostate++;
5462: A->info.nz_unneeded += (PetscReal)fshift;
5463: a->rmax = rmax;
5464: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5465: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5466: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5467: }
5468: PetscFunctionReturn(PETSC_SUCCESS);
5469: }
5471: PetscFunctionList MatSeqAIJList = NULL;
5473: /*@
5474: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5476: Collective
5478: Input Parameters:
5479: + mat - the matrix object
5480: - matype - matrix type
5482: Options Database Key:
5483: . -mat_seqaij_type <method> - for example seqaijcrl
5485: Level: intermediate
5487: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5488: @*/
5489: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5490: {
5491: PetscBool sametype;
5492: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5494: PetscFunctionBegin;
5496: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5497: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5499: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5500: PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5501: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5502: PetscFunctionReturn(PETSC_SUCCESS);
5503: }
5505: /*@C
5506: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5508: Not Collective, No Fortran Support
5510: Input Parameters:
5511: + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5512: - function - routine to convert to subtype
5514: Level: advanced
5516: Notes:
5517: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5519: Then, your matrix can be chosen with the procedural interface at runtime via the option
5520: .vb
5521: -mat_seqaij_type my_mat
5522: .ve
5524: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5525: @*/
5526: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5527: {
5528: PetscFunctionBegin;
5529: PetscCall(MatInitializePackage());
5530: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5531: PetscFunctionReturn(PETSC_SUCCESS);
5532: }
5534: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5536: /*@C
5537: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5539: Not Collective
5541: Level: advanced
5543: Note:
5544: This registers the versions of `MATSEQAIJ` for GPUs
5546: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5547: @*/
5548: PetscErrorCode MatSeqAIJRegisterAll(void)
5549: {
5550: PetscFunctionBegin;
5551: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5552: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5554: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5555: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5556: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5557: #if defined(PETSC_HAVE_MKL_SPARSE)
5558: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5559: #endif
5560: #if defined(PETSC_HAVE_CUDA)
5561: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5562: #endif
5563: #if defined(PETSC_HAVE_HIP)
5564: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5565: #endif
5566: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5567: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5568: #endif
5569: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5570: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5571: #endif
5572: PetscFunctionReturn(PETSC_SUCCESS);
5573: }
5575: /*
5576: Special version for direct calls from Fortran
5577: */
5578: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5579: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5580: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5581: #define matsetvaluesseqaij_ matsetvaluesseqaij
5582: #endif
5584: /* Change these macros so can be used in void function */
5586: /* Change these macros so can be used in void function */
5587: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5588: #undef PetscCall
5589: #define PetscCall(...) \
5590: do { \
5591: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5592: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5593: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5594: return; \
5595: } \
5596: } while (0)
5598: #undef SETERRQ
5599: #define SETERRQ(comm, ierr, ...) \
5600: do { \
5601: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5602: return; \
5603: } while (0)
5605: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5606: {
5607: Mat A = *AA;
5608: PetscInt m = *mm, n = *nn;
5609: InsertMode is = *isis;
5610: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5611: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5612: PetscInt *imax, *ai, *ailen;
5613: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5614: MatScalar *ap, value, *aa;
5615: PetscBool ignorezeroentries = a->ignorezeroentries;
5616: PetscBool roworiented = a->roworiented;
5618: PetscFunctionBegin;
5619: MatCheckPreallocated(A, 1);
5620: imax = a->imax;
5621: ai = a->i;
5622: ailen = a->ilen;
5623: aj = a->j;
5624: aa = a->a;
5626: for (k = 0; k < m; k++) { /* loop over added rows */
5627: row = im[k];
5628: if (row < 0) continue;
5629: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5630: rp = aj + ai[row];
5631: ap = aa + ai[row];
5632: rmax = imax[row];
5633: nrow = ailen[row];
5634: low = 0;
5635: high = nrow;
5636: for (l = 0; l < n; l++) { /* loop over added columns */
5637: if (in[l] < 0) continue;
5638: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5639: col = in[l];
5640: if (roworiented) value = v[l + k * n];
5641: else value = v[k + l * m];
5643: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5645: if (col <= lastcol) low = 0;
5646: else high = nrow;
5647: lastcol = col;
5648: while (high - low > 5) {
5649: t = (low + high) / 2;
5650: if (rp[t] > col) high = t;
5651: else low = t;
5652: }
5653: for (i = low; i < high; i++) {
5654: if (rp[i] > col) break;
5655: if (rp[i] == col) {
5656: if (is == ADD_VALUES) ap[i] += value;
5657: else ap[i] = value;
5658: goto noinsert;
5659: }
5660: }
5661: if (value == 0.0 && ignorezeroentries) goto noinsert;
5662: if (nonew == 1) goto noinsert;
5663: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5664: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5665: N = nrow++ - 1;
5666: a->nz++;
5667: high++;
5668: /* shift up all the later entries in this row */
5669: for (ii = N; ii >= i; ii--) {
5670: rp[ii + 1] = rp[ii];
5671: ap[ii + 1] = ap[ii];
5672: }
5673: rp[i] = col;
5674: ap[i] = value;
5675: noinsert:;
5676: low = i + 1;
5677: }
5678: ailen[row] = nrow;
5679: }
5680: PetscFunctionReturnVoid();
5681: }
5682: /* Undefining these here since they were redefined from their original definition above! No
5683: * other PETSc functions should be defined past this point, as it is impossible to recover the
5684: * original definitions */
5685: #undef PetscCall
5686: #undef SETERRQ