Actual source code: aij.c
1: /*
2: Defines the basic matrix operations for the AIJ (compressed row)
3: matrix storage format.
4: */
6: #include <../src/mat/impls/aij/seq/aij.h>
7: #include <petscblaslapack.h>
8: #include <petscbt.h>
9: #include <petsc/private/kernels/blocktranspose.h>
11: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
12: #define TYPE AIJ
13: #define TYPE_BS
14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
15: #include "../src/mat/impls/aij/seq/seqhashmat.h"
16: #undef TYPE
17: #undef TYPE_BS
19: static PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
20: {
21: PetscBool flg;
22: char type[256];
24: PetscFunctionBegin;
25: PetscObjectOptionsBegin((PetscObject)A);
26: PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg));
27: if (flg) PetscCall(MatSeqAIJSetType(A, type));
28: PetscOptionsEnd();
29: PetscFunctionReturn(PETSC_SUCCESS);
30: }
32: static PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions)
33: {
34: PetscInt i, m, n;
35: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
37: PetscFunctionBegin;
38: PetscCall(MatGetSize(A, &m, &n));
39: PetscCall(PetscArrayzero(reductions, n));
40: if (type == NORM_2) {
41: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]);
42: } else if (type == NORM_1) {
43: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
44: } else if (type == NORM_INFINITY) {
45: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]);
46: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
47: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
48: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
49: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
50: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
52: if (type == NORM_2) {
53: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
54: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
55: for (i = 0; i < n; i++) reductions[i] /= m;
56: }
57: PetscFunctionReturn(PETSC_SUCCESS);
58: }
60: static PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is)
61: {
62: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
63: PetscInt i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs;
64: const PetscInt *jj = a->j, *ii = a->i;
65: PetscInt *rows;
67: PetscFunctionBegin;
68: for (i = 0; i < m; i++) {
69: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++;
70: }
71: PetscCall(PetscMalloc1(cnt, &rows));
72: cnt = 0;
73: for (i = 0; i < m; i++) {
74: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) {
75: rows[cnt] = i;
76: cnt++;
77: }
78: }
79: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is));
80: PetscFunctionReturn(PETSC_SUCCESS);
81: }
83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows)
84: {
85: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
86: const MatScalar *aa;
87: PetscInt i, m = A->rmap->n, cnt = 0;
88: const PetscInt *ii = a->i, *jj = a->j, *diag;
89: PetscInt *rows;
91: PetscFunctionBegin;
92: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
93: PetscCall(MatMarkDiagonal_SeqAIJ(A));
94: diag = a->diag;
95: for (i = 0; i < m; i++) {
96: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++;
97: }
98: PetscCall(PetscMalloc1(cnt, &rows));
99: cnt = 0;
100: for (i = 0; i < m; i++) {
101: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i;
102: }
103: *nrows = cnt;
104: *zrows = rows;
105: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
106: PetscFunctionReturn(PETSC_SUCCESS);
107: }
109: static PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows)
110: {
111: PetscInt nrows, *rows;
113: PetscFunctionBegin;
114: *zrows = NULL;
115: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows));
116: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows));
117: PetscFunctionReturn(PETSC_SUCCESS);
118: }
120: static PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows)
121: {
122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
123: const MatScalar *aa;
124: PetscInt m = A->rmap->n, cnt = 0;
125: const PetscInt *ii;
126: PetscInt n, i, j, *rows;
128: PetscFunctionBegin;
129: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
130: *keptrows = NULL;
131: ii = a->i;
132: for (i = 0; i < m; i++) {
133: n = ii[i + 1] - ii[i];
134: if (!n) {
135: cnt++;
136: goto ok1;
137: }
138: for (j = ii[i]; j < ii[i + 1]; j++) {
139: if (aa[j] != 0.0) goto ok1;
140: }
141: cnt++;
142: ok1:;
143: }
144: if (!cnt) {
145: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
146: PetscFunctionReturn(PETSC_SUCCESS);
147: }
148: PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows));
149: cnt = 0;
150: for (i = 0; i < m; i++) {
151: n = ii[i + 1] - ii[i];
152: if (!n) continue;
153: for (j = ii[i]; j < ii[i + 1]; j++) {
154: if (aa[j] != 0.0) {
155: rows[cnt++] = i;
156: break;
157: }
158: }
159: }
160: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
161: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows));
162: PetscFunctionReturn(PETSC_SUCCESS);
163: }
165: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is)
166: {
167: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)Y->data;
168: PetscInt i, m = Y->rmap->n;
169: const PetscInt *diag;
170: MatScalar *aa;
171: const PetscScalar *v;
172: PetscBool missing;
174: PetscFunctionBegin;
175: if (Y->assembled) {
176: PetscCall(MatMissingDiagonal_SeqAIJ(Y, &missing, NULL));
177: if (!missing) {
178: diag = aij->diag;
179: PetscCall(VecGetArrayRead(D, &v));
180: PetscCall(MatSeqAIJGetArray(Y, &aa));
181: if (is == INSERT_VALUES) {
182: for (i = 0; i < m; i++) aa[diag[i]] = v[i];
183: } else {
184: for (i = 0; i < m; i++) aa[diag[i]] += v[i];
185: }
186: PetscCall(MatSeqAIJRestoreArray(Y, &aa));
187: PetscCall(VecRestoreArrayRead(D, &v));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
190: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
191: }
192: PetscCall(MatDiagonalSet_Default(Y, D, is));
193: PetscFunctionReturn(PETSC_SUCCESS);
194: }
196: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
197: {
198: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
199: PetscInt i, ishift;
201: PetscFunctionBegin;
202: if (m) *m = A->rmap->n;
203: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
204: ishift = 0;
205: if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) {
206: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja));
207: } else if (oshift == 1) {
208: PetscInt *tia;
209: PetscInt nz = a->i[A->rmap->n];
210: /* malloc space and add 1 to i and j indices */
211: PetscCall(PetscMalloc1(A->rmap->n + 1, &tia));
212: for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1;
213: *ia = tia;
214: if (ja) {
215: PetscInt *tja;
216: PetscCall(PetscMalloc1(nz + 1, &tja));
217: for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1;
218: *ja = tja;
219: }
220: } else {
221: *ia = a->i;
222: if (ja) *ja = a->j;
223: }
224: PetscFunctionReturn(PETSC_SUCCESS);
225: }
227: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
228: {
229: PetscFunctionBegin;
230: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
231: if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) {
232: PetscCall(PetscFree(*ia));
233: if (ja) PetscCall(PetscFree(*ja));
234: }
235: PetscFunctionReturn(PETSC_SUCCESS);
236: }
238: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
239: {
240: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
241: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
242: PetscInt nz = a->i[m], row, *jj, mr, col;
244: PetscFunctionBegin;
245: *nn = n;
246: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
247: if (symmetric) {
248: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, 0, oshift, (PetscInt **)ia, (PetscInt **)ja));
249: } else {
250: PetscCall(PetscCalloc1(n, &collengths));
251: PetscCall(PetscMalloc1(n + 1, &cia));
252: PetscCall(PetscMalloc1(nz, &cja));
253: jj = a->j;
254: for (i = 0; i < nz; i++) collengths[jj[i]]++;
255: cia[0] = oshift;
256: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
257: PetscCall(PetscArrayzero(collengths, n));
258: jj = a->j;
259: for (row = 0; row < m; row++) {
260: mr = a->i[row + 1] - a->i[row];
261: for (i = 0; i < mr; i++) {
262: col = *jj++;
264: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
265: }
266: }
267: PetscCall(PetscFree(collengths));
268: *ia = cia;
269: *ja = cja;
270: }
271: PetscFunctionReturn(PETSC_SUCCESS);
272: }
274: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
275: {
276: PetscFunctionBegin;
277: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
279: PetscCall(PetscFree(*ia));
280: PetscCall(PetscFree(*ja));
281: PetscFunctionReturn(PETSC_SUCCESS);
282: }
284: /*
285: MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
286: MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
287: spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
288: */
289: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
290: {
291: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
292: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
293: PetscInt nz = a->i[m], row, mr, col, tmp;
294: PetscInt *cspidx;
295: const PetscInt *jj;
297: PetscFunctionBegin;
298: *nn = n;
299: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
301: PetscCall(PetscCalloc1(n, &collengths));
302: PetscCall(PetscMalloc1(n + 1, &cia));
303: PetscCall(PetscMalloc1(nz, &cja));
304: PetscCall(PetscMalloc1(nz, &cspidx));
305: jj = a->j;
306: for (i = 0; i < nz; i++) collengths[jj[i]]++;
307: cia[0] = oshift;
308: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
309: PetscCall(PetscArrayzero(collengths, n));
310: jj = a->j;
311: for (row = 0; row < m; row++) {
312: mr = a->i[row + 1] - a->i[row];
313: for (i = 0; i < mr; i++) {
314: col = *jj++;
315: tmp = cia[col] + collengths[col]++ - oshift;
316: cspidx[tmp] = a->i[row] + i; /* index of a->j */
317: cja[tmp] = row + oshift;
318: }
319: }
320: PetscCall(PetscFree(collengths));
321: *ia = cia;
322: *ja = cja;
323: *spidx = cspidx;
324: PetscFunctionReturn(PETSC_SUCCESS);
325: }
327: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
328: {
329: PetscFunctionBegin;
330: PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
331: PetscCall(PetscFree(*spidx));
332: PetscFunctionReturn(PETSC_SUCCESS);
333: }
335: static PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[])
336: {
337: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
338: PetscInt *ai = a->i;
339: PetscScalar *aa;
341: PetscFunctionBegin;
342: PetscCall(MatSeqAIJGetArray(A, &aa));
343: PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row]));
344: PetscCall(MatSeqAIJRestoreArray(A, &aa));
345: PetscFunctionReturn(PETSC_SUCCESS);
346: }
348: /*
349: MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions
351: - a single row of values is set with each call
352: - no row or column indices are negative or (in error) larger than the number of rows or columns
353: - the values are always added to the matrix, not set
354: - no new locations are introduced in the nonzero structure of the matrix
356: This does NOT assume the global column indices are sorted
358: */
360: #include <petsc/private/isimpl.h>
361: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
362: {
363: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
364: PetscInt low, high, t, row, nrow, i, col, l;
365: const PetscInt *rp, *ai = a->i, *ailen = a->ilen, *aj = a->j;
366: PetscInt lastcol = -1;
367: MatScalar *ap, value, *aa;
368: const PetscInt *ridx = A->rmap->mapping->indices, *cidx = A->cmap->mapping->indices;
370: PetscFunctionBegin;
371: PetscCall(MatSeqAIJGetArray(A, &aa));
372: row = ridx[im[0]];
373: rp = aj + ai[row];
374: ap = aa + ai[row];
375: nrow = ailen[row];
376: low = 0;
377: high = nrow;
378: for (l = 0; l < n; l++) { /* loop over added columns */
379: col = cidx[in[l]];
380: value = v[l];
382: if (col <= lastcol) low = 0;
383: else high = nrow;
384: lastcol = col;
385: while (high - low > 5) {
386: t = (low + high) / 2;
387: if (rp[t] > col) high = t;
388: else low = t;
389: }
390: for (i = low; i < high; i++) {
391: if (rp[i] == col) {
392: ap[i] += value;
393: low = i + 1;
394: break;
395: }
396: }
397: }
398: PetscCall(MatSeqAIJRestoreArray(A, &aa));
399: return PETSC_SUCCESS;
400: }
402: PetscErrorCode MatSetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
403: {
404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
405: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
406: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
407: PetscInt *aj = a->j, nonew = a->nonew, lastcol = -1;
408: MatScalar *ap = NULL, value = 0.0, *aa;
409: PetscBool ignorezeroentries = a->ignorezeroentries;
410: PetscBool roworiented = a->roworiented;
412: PetscFunctionBegin;
413: PetscCall(MatSeqAIJGetArray(A, &aa));
414: for (k = 0; k < m; k++) { /* loop over added rows */
415: row = im[k];
416: if (row < 0) continue;
417: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
418: rp = PetscSafePointerPlusOffset(aj, ai[row]);
419: if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, ai[row]);
420: rmax = imax[row];
421: nrow = ailen[row];
422: low = 0;
423: high = nrow;
424: for (l = 0; l < n; l++) { /* loop over added columns */
425: if (in[l] < 0) continue;
426: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
427: col = in[l];
428: if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m];
429: if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;
431: if (col <= lastcol) low = 0;
432: else high = nrow;
433: lastcol = col;
434: while (high - low > 5) {
435: t = (low + high) / 2;
436: if (rp[t] > col) high = t;
437: else low = t;
438: }
439: for (i = low; i < high; i++) {
440: if (rp[i] > col) break;
441: if (rp[i] == col) {
442: if (!A->structure_only) {
443: if (is == ADD_VALUES) {
444: ap[i] += value;
445: (void)PetscLogFlops(1.0);
446: } else ap[i] = value;
447: }
448: low = i + 1;
449: goto noinsert;
450: }
451: }
452: if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
453: if (nonew == 1) goto noinsert;
454: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") in the matrix", row, col);
455: if (A->structure_only) {
456: MatSeqXAIJReallocateAIJ_structure_only(A, A->rmap->n, 1, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
457: } else {
458: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
459: }
460: N = nrow++ - 1;
461: a->nz++;
462: high++;
463: /* shift up all the later entries in this row */
464: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
465: rp[i] = col;
466: if (!A->structure_only) {
467: PetscCall(PetscArraymove(ap + i + 1, ap + i, N - i + 1));
468: ap[i] = value;
469: }
470: low = i + 1;
471: noinsert:;
472: }
473: ailen[row] = nrow;
474: }
475: PetscCall(MatSeqAIJRestoreArray(A, &aa));
476: PetscFunctionReturn(PETSC_SUCCESS);
477: }
479: static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
480: {
481: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
482: PetscInt *rp, k, row;
483: PetscInt *ai = a->i;
484: PetscInt *aj = a->j;
485: MatScalar *aa, *ap;
487: PetscFunctionBegin;
488: PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
489: PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz);
491: PetscCall(MatSeqAIJGetArray(A, &aa));
492: for (k = 0; k < m; k++) { /* loop over added rows */
493: row = im[k];
494: rp = aj + ai[row];
495: ap = PetscSafePointerPlusOffset(aa, ai[row]);
497: PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
498: if (!A->structure_only) {
499: if (v) {
500: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
501: v += n;
502: } else {
503: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
504: }
505: }
506: a->ilen[row] = n;
507: a->imax[row] = n;
508: a->i[row + 1] = a->i[row] + n;
509: a->nz += n;
510: }
511: PetscCall(MatSeqAIJRestoreArray(A, &aa));
512: PetscFunctionReturn(PETSC_SUCCESS);
513: }
515: /*@
516: MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.
518: Input Parameters:
519: + A - the `MATSEQAIJ` matrix
520: - nztotal - bound on the number of nonzeros
522: Level: advanced
524: Notes:
525: This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
526: Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used
527: as always with multiple matrix assemblies.
529: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
530: @*/
531: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
532: {
533: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
535: PetscFunctionBegin;
536: PetscCall(PetscLayoutSetUp(A->rmap));
537: PetscCall(PetscLayoutSetUp(A->cmap));
538: a->maxnz = nztotal;
539: if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
540: if (!a->ilen) {
541: PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
542: } else {
543: PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
544: }
546: /* allocate the matrix space */
547: PetscCall(PetscShmgetAllocateArray(A->rmap->n + 1, sizeof(PetscInt), (void **)&a->i));
548: PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscInt), (void **)&a->j));
549: a->free_ij = PETSC_TRUE;
550: if (A->structure_only) {
551: a->free_a = PETSC_FALSE;
552: } else {
553: PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscScalar), (void **)&a->a));
554: a->free_a = PETSC_TRUE;
555: }
556: a->i[0] = 0;
557: A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
558: A->preallocated = PETSC_TRUE;
559: PetscFunctionReturn(PETSC_SUCCESS);
560: }
562: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
563: {
564: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
565: PetscInt *rp, k, row;
566: PetscInt *ai = a->i, *ailen = a->ilen;
567: PetscInt *aj = a->j;
568: MatScalar *aa, *ap;
570: PetscFunctionBegin;
571: PetscCall(MatSeqAIJGetArray(A, &aa));
572: for (k = 0; k < m; k++) { /* loop over added rows */
573: row = im[k];
574: PetscCheck(n <= a->imax[row], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Preallocation for row %" PetscInt_FMT " does not match number of columns provided", n);
575: rp = aj + ai[row];
576: ap = aa + ai[row];
577: if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
578: if (!A->structure_only) {
579: if (v) {
580: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
581: v += n;
582: } else {
583: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
584: }
585: }
586: ailen[row] = n;
587: a->nz += n;
588: }
589: PetscCall(MatSeqAIJRestoreArray(A, &aa));
590: PetscFunctionReturn(PETSC_SUCCESS);
591: }
593: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
594: {
595: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
596: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
597: PetscInt *ai = a->i, *ailen = a->ilen;
598: const MatScalar *ap, *aa;
600: PetscFunctionBegin;
601: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
602: for (k = 0; k < m; k++) { /* loop over rows */
603: row = im[k];
604: if (row < 0) {
605: v += n;
606: continue;
607: } /* negative row */
608: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
609: rp = PetscSafePointerPlusOffset(aj, ai[row]);
610: ap = PetscSafePointerPlusOffset(aa, ai[row]);
611: nrow = ailen[row];
612: for (l = 0; l < n; l++) { /* loop over columns */
613: if (in[l] < 0) {
614: v++;
615: continue;
616: } /* negative column */
617: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
618: col = in[l];
619: high = nrow;
620: low = 0; /* assume unsorted */
621: while (high - low > 5) {
622: t = (low + high) / 2;
623: if (rp[t] > col) high = t;
624: else low = t;
625: }
626: for (i = low; i < high; i++) {
627: if (rp[i] > col) break;
628: if (rp[i] == col) {
629: *v++ = ap[i];
630: goto finished;
631: }
632: }
633: *v++ = 0.0;
634: finished:;
635: }
636: }
637: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
638: PetscFunctionReturn(PETSC_SUCCESS);
639: }
641: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
642: {
643: Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data;
644: const PetscScalar *av;
645: PetscInt header[4], M, N, m, nz, i;
646: PetscInt *rowlens;
648: PetscFunctionBegin;
649: PetscCall(PetscViewerSetUp(viewer));
651: M = mat->rmap->N;
652: N = mat->cmap->N;
653: m = mat->rmap->n;
654: nz = A->nz;
656: /* write matrix header */
657: header[0] = MAT_FILE_CLASSID;
658: header[1] = M;
659: header[2] = N;
660: header[3] = nz;
661: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
663: /* fill in and store row lengths */
664: PetscCall(PetscMalloc1(m, &rowlens));
665: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
666: if (PetscDefined(USE_DEBUG)) {
667: PetscInt mnz = 0;
669: for (i = 0; i < m; i++) mnz += rowlens[i];
670: PetscCheck(nz == mnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row lens %" PetscInt_FMT " do not sum to nz %" PetscInt_FMT, mnz, nz);
671: }
672: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
673: PetscCall(PetscFree(rowlens));
674: /* store column indices */
675: PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
676: /* store nonzero values */
677: PetscCall(MatSeqAIJGetArrayRead(mat, &av));
678: PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
679: PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));
681: /* write block size option to the viewer's .info file */
682: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
683: PetscFunctionReturn(PETSC_SUCCESS);
684: }
686: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
687: {
688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
689: PetscInt i, k, m = A->rmap->N;
691: PetscFunctionBegin;
692: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
693: for (i = 0; i < m; i++) {
694: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
695: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
696: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
697: }
698: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
699: PetscFunctionReturn(PETSC_SUCCESS);
700: }
702: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer);
704: static PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer)
705: {
706: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
707: const PetscScalar *av;
708: PetscInt i, j, m = A->rmap->n;
709: const char *name;
710: PetscViewerFormat format;
712: PetscFunctionBegin;
713: if (A->structure_only) {
714: PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
715: PetscFunctionReturn(PETSC_SUCCESS);
716: }
718: PetscCall(PetscViewerGetFormat(viewer, &format));
719: // By petsc's rule, even PETSC_VIEWER_ASCII_INFO_DETAIL doesn't print matrix entries
720: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);
722: /* trigger copy to CPU if needed */
723: PetscCall(MatSeqAIJGetArrayRead(A, &av));
724: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
725: if (format == PETSC_VIEWER_ASCII_MATLAB) {
726: PetscInt nofinalvalue = 0;
727: if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) {
728: /* Need a dummy value to ensure the dimension of the matrix. */
729: nofinalvalue = 1;
730: }
731: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
732: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n));
733: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz));
734: #if defined(PETSC_USE_COMPLEX)
735: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue));
736: #else
737: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue));
738: #endif
739: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n"));
741: for (i = 0; i < m; i++) {
742: for (j = a->i[i]; j < a->i[i + 1]; j++) {
743: #if defined(PETSC_USE_COMPLEX)
744: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", i + 1, a->j[j] + 1, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
745: #else
746: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j]));
747: #endif
748: }
749: }
750: if (nofinalvalue) {
751: #if defined(PETSC_USE_COMPLEX)
752: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.));
753: #else
754: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0));
755: #endif
756: }
757: PetscCall(PetscObjectGetName((PetscObject)A, &name));
758: PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name));
759: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
760: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
761: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
762: for (i = 0; i < m; i++) {
763: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
764: for (j = a->i[i]; j < a->i[i + 1]; j++) {
765: #if defined(PETSC_USE_COMPLEX)
766: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
767: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
768: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
769: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
770: } else if (PetscRealPart(a->a[j]) != 0.0) {
771: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
772: }
773: #else
774: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
775: #endif
776: }
777: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
778: }
779: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
780: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
781: PetscInt nzd = 0, fshift = 1, *sptr;
782: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
783: PetscCall(PetscMalloc1(m + 1, &sptr));
784: for (i = 0; i < m; i++) {
785: sptr[i] = nzd + 1;
786: for (j = a->i[i]; j < a->i[i + 1]; j++) {
787: if (a->j[j] >= i) {
788: #if defined(PETSC_USE_COMPLEX)
789: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
790: #else
791: if (a->a[j] != 0.0) nzd++;
792: #endif
793: }
794: }
795: }
796: sptr[m] = nzd + 1;
797: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd));
798: for (i = 0; i < m + 1; i += 6) {
799: if (i + 4 < m) {
800: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4], sptr[i + 5]));
801: } else if (i + 3 < m) {
802: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4]));
803: } else if (i + 2 < m) {
804: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3]));
805: } else if (i + 1 < m) {
806: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2]));
807: } else if (i < m) {
808: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1]));
809: } else {
810: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i]));
811: }
812: }
813: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
814: PetscCall(PetscFree(sptr));
815: for (i = 0; i < m; i++) {
816: for (j = a->i[i]; j < a->i[i + 1]; j++) {
817: if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift));
818: }
819: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
820: }
821: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
822: for (i = 0; i < m; i++) {
823: for (j = a->i[i]; j < a->i[i + 1]; j++) {
824: if (a->j[j] >= i) {
825: #if defined(PETSC_USE_COMPLEX)
826: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e %18.16e ", (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
827: #else
828: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j]));
829: #endif
830: }
831: }
832: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
833: }
834: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
835: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
836: PetscInt cnt = 0, jcnt;
837: PetscScalar value;
838: #if defined(PETSC_USE_COMPLEX)
839: PetscBool realonly = PETSC_TRUE;
841: for (i = 0; i < a->i[m]; i++) {
842: if (PetscImaginaryPart(a->a[i]) != 0.0) {
843: realonly = PETSC_FALSE;
844: break;
845: }
846: }
847: #endif
849: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
850: for (i = 0; i < m; i++) {
851: jcnt = 0;
852: for (j = 0; j < A->cmap->n; j++) {
853: if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) {
854: value = a->a[cnt++];
855: jcnt++;
856: } else {
857: value = 0.0;
858: }
859: #if defined(PETSC_USE_COMPLEX)
860: if (realonly) {
861: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value)));
862: } else {
863: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value)));
864: }
865: #else
866: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value));
867: #endif
868: }
869: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
870: }
871: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
872: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
873: PetscInt fshift = 1;
874: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
875: #if defined(PETSC_USE_COMPLEX)
876: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n"));
877: #else
878: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n"));
879: #endif
880: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz));
881: for (i = 0; i < m; i++) {
882: for (j = a->i[i]; j < a->i[i + 1]; j++) {
883: #if defined(PETSC_USE_COMPLEX)
884: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g %g\n", i + fshift, a->j[j] + fshift, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
885: #else
886: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j]));
887: #endif
888: }
889: }
890: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
891: } else {
892: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
893: if (A->factortype) {
894: for (i = 0; i < m; i++) {
895: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
896: /* L part */
897: for (j = a->i[i]; j < a->i[i + 1]; j++) {
898: #if defined(PETSC_USE_COMPLEX)
899: if (PetscImaginaryPart(a->a[j]) > 0.0) {
900: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
901: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
902: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
903: } else {
904: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
905: }
906: #else
907: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
908: #endif
909: }
910: /* diagonal */
911: j = a->diag[i];
912: #if defined(PETSC_USE_COMPLEX)
913: if (PetscImaginaryPart(a->a[j]) > 0.0) {
914: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)PetscImaginaryPart(1 / a->a[j])));
915: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
916: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)(-PetscImaginaryPart(1 / a->a[j]))));
917: } else {
918: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1 / a->a[j])));
919: }
920: #else
921: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1 / a->a[j])));
922: #endif
924: /* U part */
925: for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) {
926: #if defined(PETSC_USE_COMPLEX)
927: if (PetscImaginaryPart(a->a[j]) > 0.0) {
928: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
929: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
930: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
931: } else {
932: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
933: }
934: #else
935: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
936: #endif
937: }
938: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
939: }
940: } else {
941: for (i = 0; i < m; i++) {
942: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
943: for (j = a->i[i]; j < a->i[i + 1]; j++) {
944: #if defined(PETSC_USE_COMPLEX)
945: if (PetscImaginaryPart(a->a[j]) > 0.0) {
946: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
947: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
948: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
949: } else {
950: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
951: }
952: #else
953: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
954: #endif
955: }
956: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
957: }
958: }
959: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
960: }
961: PetscCall(PetscViewerFlush(viewer));
962: PetscFunctionReturn(PETSC_SUCCESS);
963: }
965: #include <petscdraw.h>
966: static PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
967: {
968: Mat A = (Mat)Aa;
969: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
970: PetscInt i, j, m = A->rmap->n;
971: int color;
972: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
973: PetscViewer viewer;
974: PetscViewerFormat format;
975: const PetscScalar *aa;
977: PetscFunctionBegin;
978: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
979: PetscCall(PetscViewerGetFormat(viewer, &format));
980: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
982: /* loop over matrix elements drawing boxes */
983: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
984: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
985: PetscDrawCollectiveBegin(draw);
986: /* Blue for negative, Cyan for zero and Red for positive */
987: color = PETSC_DRAW_BLUE;
988: for (i = 0; i < m; i++) {
989: y_l = m - i - 1.0;
990: y_r = y_l + 1.0;
991: for (j = a->i[i]; j < a->i[i + 1]; j++) {
992: x_l = a->j[j];
993: x_r = x_l + 1.0;
994: if (PetscRealPart(aa[j]) >= 0.) continue;
995: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
996: }
997: }
998: color = PETSC_DRAW_CYAN;
999: for (i = 0; i < m; i++) {
1000: y_l = m - i - 1.0;
1001: y_r = y_l + 1.0;
1002: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1003: x_l = a->j[j];
1004: x_r = x_l + 1.0;
1005: if (aa[j] != 0.) continue;
1006: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1007: }
1008: }
1009: color = PETSC_DRAW_RED;
1010: for (i = 0; i < m; i++) {
1011: y_l = m - i - 1.0;
1012: y_r = y_l + 1.0;
1013: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1014: x_l = a->j[j];
1015: x_r = x_l + 1.0;
1016: if (PetscRealPart(aa[j]) <= 0.) continue;
1017: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1018: }
1019: }
1020: PetscDrawCollectiveEnd(draw);
1021: } else {
1022: /* use contour shading to indicate magnitude of values */
1023: /* first determine max of all nonzero values */
1024: PetscReal minv = 0.0, maxv = 0.0;
1025: PetscInt nz = a->nz, count = 0;
1026: PetscDraw popup;
1028: for (i = 0; i < nz; i++) {
1029: if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1030: }
1031: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1032: PetscCall(PetscDrawGetPopup(draw, &popup));
1033: PetscCall(PetscDrawScalePopup(popup, minv, maxv));
1035: PetscDrawCollectiveBegin(draw);
1036: for (i = 0; i < m; i++) {
1037: y_l = m - i - 1.0;
1038: y_r = y_l + 1.0;
1039: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1040: x_l = a->j[j];
1041: x_r = x_l + 1.0;
1042: color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1043: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1044: count++;
1045: }
1046: }
1047: PetscDrawCollectiveEnd(draw);
1048: }
1049: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1050: PetscFunctionReturn(PETSC_SUCCESS);
1051: }
1053: #include <petscdraw.h>
1054: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1055: {
1056: PetscDraw draw;
1057: PetscReal xr, yr, xl, yl, h, w;
1058: PetscBool isnull;
1060: PetscFunctionBegin;
1061: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1062: PetscCall(PetscDrawIsNull(draw, &isnull));
1063: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1065: xr = A->cmap->n;
1066: yr = A->rmap->n;
1067: h = yr / 10.0;
1068: w = xr / 10.0;
1069: xr += w;
1070: yr += h;
1071: xl = -w;
1072: yl = -h;
1073: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1074: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1075: PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1076: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1077: PetscCall(PetscDrawSave(draw));
1078: PetscFunctionReturn(PETSC_SUCCESS);
1079: }
1081: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1082: {
1083: PetscBool iascii, isbinary, isdraw;
1085: PetscFunctionBegin;
1086: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1087: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1088: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1089: if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1090: else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1091: else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1092: PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1093: PetscFunctionReturn(PETSC_SUCCESS);
1094: }
1096: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1097: {
1098: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1099: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1100: PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n;
1101: MatScalar *aa = a->a, *ap;
1102: PetscReal ratio = 0.6;
1104: PetscFunctionBegin;
1105: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
1106: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1107: if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1108: /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1109: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1110: PetscFunctionReturn(PETSC_SUCCESS);
1111: }
1113: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1114: for (i = 1; i < m; i++) {
1115: /* move each row back by the amount of empty slots (fshift) before it*/
1116: fshift += imax[i - 1] - ailen[i - 1];
1117: rmax = PetscMax(rmax, ailen[i]);
1118: if (fshift) {
1119: ip = aj + ai[i];
1120: ap = aa + ai[i];
1121: N = ailen[i];
1122: PetscCall(PetscArraymove(ip - fshift, ip, N));
1123: if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1124: }
1125: ai[i] = ai[i - 1] + ailen[i - 1];
1126: }
1127: if (m) {
1128: fshift += imax[m - 1] - ailen[m - 1];
1129: ai[m] = ai[m - 1] + ailen[m - 1];
1130: }
1131: /* reset ilen and imax for each row */
1132: a->nonzerorowcnt = 0;
1133: if (A->structure_only) {
1134: PetscCall(PetscFree(a->imax));
1135: PetscCall(PetscFree(a->ilen));
1136: } else { /* !A->structure_only */
1137: for (i = 0; i < m; i++) {
1138: ailen[i] = imax[i] = ai[i + 1] - ai[i];
1139: a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1140: }
1141: }
1142: a->nz = ai[m];
1143: PetscCheck(!fshift || a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, fshift);
1144: PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
1145: a->diagonaldense = PETSC_TRUE;
1146: n = PetscMin(A->rmap->n, A->cmap->n);
1147: for (i = 0; i < n; i++) {
1148: if (a->diag[i] >= ai[i + 1]) {
1149: a->diagonaldense = PETSC_FALSE;
1150: break;
1151: }
1152: }
1153: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded,%" PetscInt_FMT " used\n", m, A->cmap->n, fshift, a->nz));
1154: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1155: PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));
1157: A->info.mallocs += a->reallocs;
1158: a->reallocs = 0;
1159: A->info.nz_unneeded = (PetscReal)fshift;
1160: a->rmax = rmax;
1162: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1163: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1164: PetscFunctionReturn(PETSC_SUCCESS);
1165: }
1167: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1168: {
1169: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1170: PetscInt i, nz = a->nz;
1171: MatScalar *aa;
1173: PetscFunctionBegin;
1174: PetscCall(MatSeqAIJGetArray(A, &aa));
1175: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1176: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1177: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1178: PetscFunctionReturn(PETSC_SUCCESS);
1179: }
1181: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1182: {
1183: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1184: PetscInt i, nz = a->nz;
1185: MatScalar *aa;
1187: PetscFunctionBegin;
1188: PetscCall(MatSeqAIJGetArray(A, &aa));
1189: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1190: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1191: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1192: PetscFunctionReturn(PETSC_SUCCESS);
1193: }
1195: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1196: {
1197: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1198: MatScalar *aa;
1200: PetscFunctionBegin;
1201: PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1202: PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1203: PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1204: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1205: PetscFunctionReturn(PETSC_SUCCESS);
1206: }
1208: static PetscErrorCode MatReset_SeqAIJ(Mat A)
1209: {
1210: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1212: PetscFunctionBegin;
1213: if (A->hash_active) {
1214: A->ops[0] = a->cops;
1215: PetscCall(PetscHMapIJVDestroy(&a->ht));
1216: PetscCall(PetscFree(a->dnz));
1217: A->hash_active = PETSC_FALSE;
1218: }
1220: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1221: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1222: PetscCall(ISDestroy(&a->row));
1223: PetscCall(ISDestroy(&a->col));
1224: PetscCall(PetscFree(a->diag));
1225: PetscCall(PetscFree(a->ibdiag));
1226: PetscCall(PetscFree(a->imax));
1227: PetscCall(PetscFree(a->ilen));
1228: PetscCall(PetscFree(a->ipre));
1229: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1230: PetscCall(PetscFree(a->solve_work));
1231: PetscCall(ISDestroy(&a->icol));
1232: PetscCall(PetscFree(a->saved_values));
1233: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1234: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1235: PetscFunctionReturn(PETSC_SUCCESS);
1236: }
1238: static PetscErrorCode MatResetHash_SeqAIJ(Mat A)
1239: {
1240: PetscFunctionBegin;
1241: PetscCall(MatReset_SeqAIJ(A));
1242: PetscCall(MatCreate_SeqAIJ_Inode(A));
1243: PetscCall(MatSetUp_Seq_Hash(A));
1244: A->nonzerostate++;
1245: PetscFunctionReturn(PETSC_SUCCESS);
1246: }
1248: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1249: {
1250: PetscFunctionBegin;
1251: PetscCall(MatReset_SeqAIJ(A));
1252: PetscCall(PetscFree(A->data));
1254: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1255: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1256: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1257: users reusing the matrix object with different data to incur in obscure segmentation faults
1258: due to different matrix sizes */
1259: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1261: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1262: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1263: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1265: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1266: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1267: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1268: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1270: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1271: #if defined(PETSC_HAVE_MKL_SPARSE)
1272: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1273: #endif
1274: #if defined(PETSC_HAVE_CUDA)
1275: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1276: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1277: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1278: #endif
1279: #if defined(PETSC_HAVE_HIP)
1280: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1282: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1283: #endif
1284: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1285: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1286: #endif
1287: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1288: #if defined(PETSC_HAVE_ELEMENTAL)
1289: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1290: #endif
1291: #if defined(PETSC_HAVE_SCALAPACK)
1292: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1293: #endif
1294: #if defined(PETSC_HAVE_HYPRE)
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1296: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1297: #endif
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1302: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1303: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1304: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1305: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetHash_C", NULL));
1306: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1307: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1308: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1309: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1310: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1311: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1312: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1313: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1314: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1315: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1316: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1317: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1318: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1319: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1320: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1321: PetscFunctionReturn(PETSC_SUCCESS);
1322: }
1324: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1325: {
1326: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1328: PetscFunctionBegin;
1329: switch (op) {
1330: case MAT_ROW_ORIENTED:
1331: a->roworiented = flg;
1332: break;
1333: case MAT_KEEP_NONZERO_PATTERN:
1334: a->keepnonzeropattern = flg;
1335: break;
1336: case MAT_NEW_NONZERO_LOCATIONS:
1337: a->nonew = (flg ? 0 : 1);
1338: break;
1339: case MAT_NEW_NONZERO_LOCATION_ERR:
1340: a->nonew = (flg ? -1 : 0);
1341: break;
1342: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1343: a->nonew = (flg ? -2 : 0);
1344: break;
1345: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1346: a->nounused = (flg ? -1 : 0);
1347: break;
1348: case MAT_IGNORE_ZERO_ENTRIES:
1349: a->ignorezeroentries = flg;
1350: break;
1351: case MAT_USE_INODES:
1352: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1353: break;
1354: case MAT_SUBMAT_SINGLEIS:
1355: A->submat_singleis = flg;
1356: break;
1357: case MAT_SORTED_FULL:
1358: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1359: else A->ops->setvalues = MatSetValues_SeqAIJ;
1360: break;
1361: case MAT_FORM_EXPLICIT_TRANSPOSE:
1362: A->form_explicit_transpose = flg;
1363: break;
1364: default:
1365: break;
1366: }
1367: PetscFunctionReturn(PETSC_SUCCESS);
1368: }
1370: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1371: {
1372: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1373: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1374: PetscScalar *x;
1375: const PetscScalar *aa;
1377: PetscFunctionBegin;
1378: PetscCall(VecGetLocalSize(v, &n));
1379: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1380: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1381: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1382: PetscInt *diag = a->diag;
1383: PetscCall(VecGetArrayWrite(v, &x));
1384: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1385: PetscCall(VecRestoreArrayWrite(v, &x));
1386: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1387: PetscFunctionReturn(PETSC_SUCCESS);
1388: }
1390: PetscCall(VecGetArrayWrite(v, &x));
1391: for (i = 0; i < n; i++) {
1392: x[i] = 0.0;
1393: for (j = ai[i]; j < ai[i + 1]; j++) {
1394: if (aj[j] == i) {
1395: x[i] = aa[j];
1396: break;
1397: }
1398: }
1399: }
1400: PetscCall(VecRestoreArrayWrite(v, &x));
1401: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1402: PetscFunctionReturn(PETSC_SUCCESS);
1403: }
1405: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1406: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1407: {
1408: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1409: const MatScalar *aa;
1410: PetscScalar *y;
1411: const PetscScalar *x;
1412: PetscInt m = A->rmap->n;
1413: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1414: const MatScalar *v;
1415: PetscScalar alpha;
1416: PetscInt n, i, j;
1417: const PetscInt *idx, *ii, *ridx = NULL;
1418: Mat_CompressedRow cprow = a->compressedrow;
1419: PetscBool usecprow = cprow.use;
1420: #endif
1422: PetscFunctionBegin;
1423: if (zz != yy) PetscCall(VecCopy(zz, yy));
1424: PetscCall(VecGetArrayRead(xx, &x));
1425: PetscCall(VecGetArray(yy, &y));
1426: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1428: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1429: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1430: #else
1431: if (usecprow) {
1432: m = cprow.nrows;
1433: ii = cprow.i;
1434: ridx = cprow.rindex;
1435: } else {
1436: ii = a->i;
1437: }
1438: for (i = 0; i < m; i++) {
1439: idx = a->j + ii[i];
1440: v = aa + ii[i];
1441: n = ii[i + 1] - ii[i];
1442: if (usecprow) {
1443: alpha = x[ridx[i]];
1444: } else {
1445: alpha = x[i];
1446: }
1447: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1448: }
1449: #endif
1450: PetscCall(PetscLogFlops(2.0 * a->nz));
1451: PetscCall(VecRestoreArrayRead(xx, &x));
1452: PetscCall(VecRestoreArray(yy, &y));
1453: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1454: PetscFunctionReturn(PETSC_SUCCESS);
1455: }
1457: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1458: {
1459: PetscFunctionBegin;
1460: PetscCall(VecSet(yy, 0.0));
1461: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1462: PetscFunctionReturn(PETSC_SUCCESS);
1463: }
1465: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1467: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1468: {
1469: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1470: PetscScalar *y;
1471: const PetscScalar *x;
1472: const MatScalar *a_a;
1473: PetscInt m = A->rmap->n;
1474: const PetscInt *ii, *ridx = NULL;
1475: PetscBool usecprow = a->compressedrow.use;
1477: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1478: #pragma disjoint(*x, *y, *aa)
1479: #endif
1481: PetscFunctionBegin;
1482: if (a->inode.use && a->inode.checked) {
1483: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1484: PetscFunctionReturn(PETSC_SUCCESS);
1485: }
1486: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1487: PetscCall(VecGetArrayRead(xx, &x));
1488: PetscCall(VecGetArray(yy, &y));
1489: ii = a->i;
1490: if (usecprow) { /* use compressed row format */
1491: PetscCall(PetscArrayzero(y, m));
1492: m = a->compressedrow.nrows;
1493: ii = a->compressedrow.i;
1494: ridx = a->compressedrow.rindex;
1495: PetscPragmaUseOMPKernels(parallel for)
1496: for (PetscInt i = 0; i < m; i++) {
1497: PetscInt n = ii[i + 1] - ii[i];
1498: const PetscInt *aj = a->j + ii[i];
1499: const PetscScalar *aa = a_a + ii[i];
1500: PetscScalar sum = 0.0;
1501: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1502: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1503: y[*ridx++] = sum;
1504: }
1505: } else { /* do not use compressed row format */
1506: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1507: fortranmultaij_(&m, x, ii, a->j, a_a, y);
1508: #else
1509: PetscPragmaUseOMPKernels(parallel for)
1510: for (PetscInt i = 0; i < m; i++) {
1511: PetscInt n = ii[i + 1] - ii[i];
1512: const PetscInt *aj = a->j + ii[i];
1513: const PetscScalar *aa = a_a + ii[i];
1514: PetscScalar sum = 0.0;
1515: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1516: y[i] = sum;
1517: }
1518: #endif
1519: }
1520: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1521: PetscCall(VecRestoreArrayRead(xx, &x));
1522: PetscCall(VecRestoreArray(yy, &y));
1523: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1524: PetscFunctionReturn(PETSC_SUCCESS);
1525: }
1527: // HACK!!!!! Used by src/mat/tests/ex170.c
1528: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1529: {
1530: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1531: PetscScalar *y;
1532: const PetscScalar *x;
1533: const MatScalar *aa, *a_a;
1534: PetscInt m = A->rmap->n;
1535: const PetscInt *aj, *ii, *ridx = NULL;
1536: PetscInt n, i, nonzerorow = 0;
1537: PetscScalar sum;
1538: PetscBool usecprow = a->compressedrow.use;
1540: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1541: #pragma disjoint(*x, *y, *aa)
1542: #endif
1544: PetscFunctionBegin;
1545: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1546: PetscCall(VecGetArrayRead(xx, &x));
1547: PetscCall(VecGetArray(yy, &y));
1548: if (usecprow) { /* use compressed row format */
1549: m = a->compressedrow.nrows;
1550: ii = a->compressedrow.i;
1551: ridx = a->compressedrow.rindex;
1552: for (i = 0; i < m; i++) {
1553: n = ii[i + 1] - ii[i];
1554: aj = a->j + ii[i];
1555: aa = a_a + ii[i];
1556: sum = 0.0;
1557: nonzerorow += (n > 0);
1558: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1559: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1560: y[*ridx++] = sum;
1561: }
1562: } else { /* do not use compressed row format */
1563: ii = a->i;
1564: for (i = 0; i < m; i++) {
1565: n = ii[i + 1] - ii[i];
1566: aj = a->j + ii[i];
1567: aa = a_a + ii[i];
1568: sum = 0.0;
1569: nonzerorow += (n > 0);
1570: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1571: y[i] = sum;
1572: }
1573: }
1574: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1575: PetscCall(VecRestoreArrayRead(xx, &x));
1576: PetscCall(VecRestoreArray(yy, &y));
1577: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1578: PetscFunctionReturn(PETSC_SUCCESS);
1579: }
1581: // HACK!!!!! Used by src/mat/tests/ex170.c
1582: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1583: {
1584: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1585: PetscScalar *y, *z;
1586: const PetscScalar *x;
1587: const MatScalar *aa, *a_a;
1588: PetscInt m = A->rmap->n, *aj, *ii;
1589: PetscInt n, i, *ridx = NULL;
1590: PetscScalar sum;
1591: PetscBool usecprow = a->compressedrow.use;
1593: PetscFunctionBegin;
1594: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1595: PetscCall(VecGetArrayRead(xx, &x));
1596: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1597: if (usecprow) { /* use compressed row format */
1598: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1599: m = a->compressedrow.nrows;
1600: ii = a->compressedrow.i;
1601: ridx = a->compressedrow.rindex;
1602: for (i = 0; i < m; i++) {
1603: n = ii[i + 1] - ii[i];
1604: aj = a->j + ii[i];
1605: aa = a_a + ii[i];
1606: sum = y[*ridx];
1607: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1608: z[*ridx++] = sum;
1609: }
1610: } else { /* do not use compressed row format */
1611: ii = a->i;
1612: for (i = 0; i < m; i++) {
1613: n = ii[i + 1] - ii[i];
1614: aj = a->j + ii[i];
1615: aa = a_a + ii[i];
1616: sum = y[i];
1617: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1618: z[i] = sum;
1619: }
1620: }
1621: PetscCall(PetscLogFlops(2.0 * a->nz));
1622: PetscCall(VecRestoreArrayRead(xx, &x));
1623: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1624: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1625: PetscFunctionReturn(PETSC_SUCCESS);
1626: }
1628: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1629: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1630: {
1631: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1632: PetscScalar *y, *z;
1633: const PetscScalar *x;
1634: const MatScalar *a_a;
1635: const PetscInt *ii, *ridx = NULL;
1636: PetscInt m = A->rmap->n;
1637: PetscBool usecprow = a->compressedrow.use;
1639: PetscFunctionBegin;
1640: if (a->inode.use && a->inode.checked) {
1641: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1642: PetscFunctionReturn(PETSC_SUCCESS);
1643: }
1644: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1645: PetscCall(VecGetArrayRead(xx, &x));
1646: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1647: if (usecprow) { /* use compressed row format */
1648: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1649: m = a->compressedrow.nrows;
1650: ii = a->compressedrow.i;
1651: ridx = a->compressedrow.rindex;
1652: for (PetscInt i = 0; i < m; i++) {
1653: PetscInt n = ii[i + 1] - ii[i];
1654: const PetscInt *aj = a->j + ii[i];
1655: const PetscScalar *aa = a_a + ii[i];
1656: PetscScalar sum = y[*ridx];
1657: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1658: z[*ridx++] = sum;
1659: }
1660: } else { /* do not use compressed row format */
1661: ii = a->i;
1662: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1663: fortranmultaddaij_(&m, x, ii, a->j, a_a, y, z);
1664: #else
1665: PetscPragmaUseOMPKernels(parallel for)
1666: for (PetscInt i = 0; i < m; i++) {
1667: PetscInt n = ii[i + 1] - ii[i];
1668: const PetscInt *aj = a->j + ii[i];
1669: const PetscScalar *aa = a_a + ii[i];
1670: PetscScalar sum = y[i];
1671: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1672: z[i] = sum;
1673: }
1674: #endif
1675: }
1676: PetscCall(PetscLogFlops(2.0 * a->nz));
1677: PetscCall(VecRestoreArrayRead(xx, &x));
1678: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1679: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1680: PetscFunctionReturn(PETSC_SUCCESS);
1681: }
1683: /*
1684: Adds diagonal pointers to sparse matrix nonzero structure.
1685: */
1686: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1687: {
1688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1689: PetscInt i, j, m = A->rmap->n;
1690: PetscBool alreadySet = PETSC_TRUE;
1692: PetscFunctionBegin;
1693: if (!a->diag) {
1694: PetscCall(PetscMalloc1(m, &a->diag));
1695: alreadySet = PETSC_FALSE;
1696: }
1697: for (i = 0; i < A->rmap->n; i++) {
1698: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1699: if (alreadySet) {
1700: PetscInt pos = a->diag[i];
1701: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1702: }
1704: a->diag[i] = a->i[i + 1];
1705: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1706: if (a->j[j] == i) {
1707: a->diag[i] = j;
1708: break;
1709: }
1710: }
1711: }
1712: PetscFunctionReturn(PETSC_SUCCESS);
1713: }
1715: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1716: {
1717: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1718: const PetscInt *diag = (const PetscInt *)a->diag;
1719: const PetscInt *ii = (const PetscInt *)a->i;
1720: PetscInt i, *mdiag = NULL;
1721: PetscInt cnt = 0; /* how many diagonals are missing */
1723: PetscFunctionBegin;
1724: if (!A->preallocated || !a->nz) {
1725: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1726: PetscCall(MatShift_Basic(A, v));
1727: PetscFunctionReturn(PETSC_SUCCESS);
1728: }
1730: if (a->diagonaldense) {
1731: cnt = 0;
1732: } else {
1733: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1734: for (i = 0; i < A->rmap->n; i++) {
1735: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1736: cnt++;
1737: mdiag[i] = 1;
1738: }
1739: }
1740: }
1741: if (!cnt) {
1742: PetscCall(MatShift_Basic(A, v));
1743: } else {
1744: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1745: PetscInt *oldj = a->j, *oldi = a->i;
1746: PetscBool free_a = a->free_a, free_ij = a->free_ij;
1747: const PetscScalar *Aa;
1749: PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1750: PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));
1752: a->a = NULL;
1753: a->j = NULL;
1754: a->i = NULL;
1755: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1756: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1757: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1759: /* copy old values into new matrix data structure */
1760: for (i = 0; i < A->rmap->n; i++) {
1761: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1762: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1763: }
1764: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1765: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1766: if (free_a) PetscCall(PetscShmgetDeallocateArray((void **)&olda));
1767: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldj));
1768: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldi));
1769: }
1770: PetscCall(PetscFree(mdiag));
1771: a->diagonaldense = PETSC_TRUE;
1772: PetscFunctionReturn(PETSC_SUCCESS);
1773: }
1775: /*
1776: Checks for missing diagonals
1777: */
1778: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1779: {
1780: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1781: PetscInt *diag, *ii = a->i, i;
1783: PetscFunctionBegin;
1784: *missing = PETSC_FALSE;
1785: if (A->rmap->n > 0 && !ii) {
1786: *missing = PETSC_TRUE;
1787: if (d) *d = 0;
1788: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1789: } else {
1790: PetscInt n;
1791: n = PetscMin(A->rmap->n, A->cmap->n);
1792: diag = a->diag;
1793: for (i = 0; i < n; i++) {
1794: if (diag[i] >= ii[i + 1]) {
1795: *missing = PETSC_TRUE;
1796: if (d) *d = i;
1797: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1798: break;
1799: }
1800: }
1801: }
1802: PetscFunctionReturn(PETSC_SUCCESS);
1803: }
1805: #include <petscblaslapack.h>
1806: #include <petsc/private/kernels/blockinvert.h>
1808: /*
1809: Note that values is allocated externally by the PC and then passed into this routine
1810: */
1811: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1812: {
1813: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1814: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1815: const PetscReal shift = 0.0;
1816: PetscInt ipvt[5];
1817: PetscCount flops = 0;
1818: PetscScalar work[25], *v_work;
1820: PetscFunctionBegin;
1821: allowzeropivot = PetscNot(A->erroriffailure);
1822: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1823: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1824: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1825: PetscCall(PetscMalloc1(bsizemax, &indx));
1826: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1827: ncnt = 0;
1828: for (i = 0; i < nblocks; i++) {
1829: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1830: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1831: switch (bsizes[i]) {
1832: case 1:
1833: *diag = 1.0 / (*diag);
1834: break;
1835: case 2:
1836: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1837: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1838: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1839: break;
1840: case 3:
1841: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1842: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1843: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1844: break;
1845: case 4:
1846: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1847: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1848: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1849: break;
1850: case 5:
1851: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1852: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1853: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1854: break;
1855: case 6:
1856: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1857: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1858: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1859: break;
1860: case 7:
1861: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1862: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1863: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1864: break;
1865: default:
1866: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1867: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1868: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1869: }
1870: ncnt += bsizes[i];
1871: diag += bsizes[i] * bsizes[i];
1872: flops += 2 * PetscPowInt64(bsizes[i], 3) / 3;
1873: }
1874: PetscCall(PetscLogFlops(flops));
1875: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1876: PetscCall(PetscFree(indx));
1877: PetscFunctionReturn(PETSC_SUCCESS);
1878: }
1880: /*
1881: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1882: */
1883: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1884: {
1885: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1886: PetscInt i, *diag, m = A->rmap->n;
1887: const MatScalar *v;
1888: PetscScalar *idiag, *mdiag;
1890: PetscFunctionBegin;
1891: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1892: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1893: diag = a->diag;
1894: if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }
1896: mdiag = a->mdiag;
1897: idiag = a->idiag;
1898: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1899: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1900: for (i = 0; i < m; i++) {
1901: mdiag[i] = v[diag[i]];
1902: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1903: if (PetscRealPart(fshift)) {
1904: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1905: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1906: A->factorerror_zeropivot_value = 0.0;
1907: A->factorerror_zeropivot_row = i;
1908: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1909: }
1910: idiag[i] = 1.0 / v[diag[i]];
1911: }
1912: PetscCall(PetscLogFlops(m));
1913: } else {
1914: for (i = 0; i < m; i++) {
1915: mdiag[i] = v[diag[i]];
1916: idiag[i] = omega / (fshift + v[diag[i]]);
1917: }
1918: PetscCall(PetscLogFlops(2.0 * m));
1919: }
1920: a->idiagvalid = PETSC_TRUE;
1921: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1922: PetscFunctionReturn(PETSC_SUCCESS);
1923: }
1925: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1926: {
1927: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1928: PetscScalar *x, d, sum, *t, scale;
1929: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1930: const PetscScalar *b, *bs, *xb, *ts;
1931: PetscInt n, m = A->rmap->n, i;
1932: const PetscInt *idx, *diag;
1934: PetscFunctionBegin;
1935: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1936: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1937: PetscFunctionReturn(PETSC_SUCCESS);
1938: }
1939: its = its * lits;
1941: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1942: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1943: a->fshift = fshift;
1944: a->omega = omega;
1946: diag = a->diag;
1947: t = a->ssor_work;
1948: idiag = a->idiag;
1949: mdiag = a->mdiag;
1951: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1952: PetscCall(VecGetArray(xx, &x));
1953: PetscCall(VecGetArrayRead(bb, &b));
1954: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1955: if (flag == SOR_APPLY_UPPER) {
1956: /* apply (U + D/omega) to the vector */
1957: bs = b;
1958: for (i = 0; i < m; i++) {
1959: d = fshift + mdiag[i];
1960: n = a->i[i + 1] - diag[i] - 1;
1961: idx = a->j + diag[i] + 1;
1962: v = aa + diag[i] + 1;
1963: sum = b[i] * d / omega;
1964: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1965: x[i] = sum;
1966: }
1967: PetscCall(VecRestoreArray(xx, &x));
1968: PetscCall(VecRestoreArrayRead(bb, &b));
1969: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1970: PetscCall(PetscLogFlops(a->nz));
1971: PetscFunctionReturn(PETSC_SUCCESS);
1972: }
1974: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1975: if (flag & SOR_EISENSTAT) {
1976: /* Let A = L + U + D; where L is lower triangular,
1977: U is upper triangular, E = D/omega; This routine applies
1979: (L + E)^{-1} A (U + E)^{-1}
1981: to a vector efficiently using Eisenstat's trick.
1982: */
1983: scale = (2.0 / omega) - 1.0;
1985: /* x = (E + U)^{-1} b */
1986: for (i = m - 1; i >= 0; i--) {
1987: n = a->i[i + 1] - diag[i] - 1;
1988: idx = a->j + diag[i] + 1;
1989: v = aa + diag[i] + 1;
1990: sum = b[i];
1991: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1992: x[i] = sum * idiag[i];
1993: }
1995: /* t = b - (2*E - D)x */
1996: v = aa;
1997: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
1999: /* t = (E + L)^{-1}t */
2000: ts = t;
2001: diag = a->diag;
2002: for (i = 0; i < m; i++) {
2003: n = diag[i] - a->i[i];
2004: idx = a->j + a->i[i];
2005: v = aa + a->i[i];
2006: sum = t[i];
2007: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2008: t[i] = sum * idiag[i];
2009: /* x = x + t */
2010: x[i] += t[i];
2011: }
2013: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2014: PetscCall(VecRestoreArray(xx, &x));
2015: PetscCall(VecRestoreArrayRead(bb, &b));
2016: PetscFunctionReturn(PETSC_SUCCESS);
2017: }
2018: if (flag & SOR_ZERO_INITIAL_GUESS) {
2019: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2020: for (i = 0; i < m; i++) {
2021: n = diag[i] - a->i[i];
2022: idx = a->j + a->i[i];
2023: v = aa + a->i[i];
2024: sum = b[i];
2025: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2026: t[i] = sum;
2027: x[i] = sum * idiag[i];
2028: }
2029: xb = t;
2030: PetscCall(PetscLogFlops(a->nz));
2031: } else xb = b;
2032: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2033: for (i = m - 1; i >= 0; i--) {
2034: n = a->i[i + 1] - diag[i] - 1;
2035: idx = a->j + diag[i] + 1;
2036: v = aa + diag[i] + 1;
2037: sum = xb[i];
2038: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2039: if (xb == b) {
2040: x[i] = sum * idiag[i];
2041: } else {
2042: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2043: }
2044: }
2045: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2046: }
2047: its--;
2048: }
2049: while (its--) {
2050: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2051: for (i = 0; i < m; i++) {
2052: /* lower */
2053: n = diag[i] - a->i[i];
2054: idx = a->j + a->i[i];
2055: v = aa + a->i[i];
2056: sum = b[i];
2057: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2058: t[i] = sum; /* save application of the lower-triangular part */
2059: /* upper */
2060: n = a->i[i + 1] - diag[i] - 1;
2061: idx = a->j + diag[i] + 1;
2062: v = aa + diag[i] + 1;
2063: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2064: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2065: }
2066: xb = t;
2067: PetscCall(PetscLogFlops(2.0 * a->nz));
2068: } else xb = b;
2069: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2070: for (i = m - 1; i >= 0; i--) {
2071: sum = xb[i];
2072: if (xb == b) {
2073: /* whole matrix (no checkpointing available) */
2074: n = a->i[i + 1] - a->i[i];
2075: idx = a->j + a->i[i];
2076: v = aa + a->i[i];
2077: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2078: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2079: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2080: n = a->i[i + 1] - diag[i] - 1;
2081: idx = a->j + diag[i] + 1;
2082: v = aa + diag[i] + 1;
2083: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2084: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2085: }
2086: }
2087: if (xb == b) {
2088: PetscCall(PetscLogFlops(2.0 * a->nz));
2089: } else {
2090: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2091: }
2092: }
2093: }
2094: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2095: PetscCall(VecRestoreArray(xx, &x));
2096: PetscCall(VecRestoreArrayRead(bb, &b));
2097: PetscFunctionReturn(PETSC_SUCCESS);
2098: }
2100: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2101: {
2102: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2104: PetscFunctionBegin;
2105: info->block_size = 1.0;
2106: info->nz_allocated = a->maxnz;
2107: info->nz_used = a->nz;
2108: info->nz_unneeded = (a->maxnz - a->nz);
2109: info->assemblies = A->num_ass;
2110: info->mallocs = A->info.mallocs;
2111: info->memory = 0; /* REVIEW ME */
2112: if (A->factortype) {
2113: info->fill_ratio_given = A->info.fill_ratio_given;
2114: info->fill_ratio_needed = A->info.fill_ratio_needed;
2115: info->factor_mallocs = A->info.factor_mallocs;
2116: } else {
2117: info->fill_ratio_given = 0;
2118: info->fill_ratio_needed = 0;
2119: info->factor_mallocs = 0;
2120: }
2121: PetscFunctionReturn(PETSC_SUCCESS);
2122: }
2124: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2125: {
2126: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2127: PetscInt i, m = A->rmap->n - 1;
2128: const PetscScalar *xx;
2129: PetscScalar *bb, *aa;
2130: PetscInt d = 0;
2132: PetscFunctionBegin;
2133: if (x && b) {
2134: PetscCall(VecGetArrayRead(x, &xx));
2135: PetscCall(VecGetArray(b, &bb));
2136: for (i = 0; i < N; i++) {
2137: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2138: if (rows[i] >= A->cmap->n) continue;
2139: bb[rows[i]] = diag * xx[rows[i]];
2140: }
2141: PetscCall(VecRestoreArrayRead(x, &xx));
2142: PetscCall(VecRestoreArray(b, &bb));
2143: }
2145: PetscCall(MatSeqAIJGetArray(A, &aa));
2146: if (a->keepnonzeropattern) {
2147: for (i = 0; i < N; i++) {
2148: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2149: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2150: }
2151: if (diag != 0.0) {
2152: for (i = 0; i < N; i++) {
2153: d = rows[i];
2154: if (rows[i] >= A->cmap->n) continue;
2155: 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);
2156: }
2157: for (i = 0; i < N; i++) {
2158: if (rows[i] >= A->cmap->n) continue;
2159: aa[a->diag[rows[i]]] = diag;
2160: }
2161: }
2162: } else {
2163: if (diag != 0.0) {
2164: for (i = 0; i < N; i++) {
2165: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2166: if (a->ilen[rows[i]] > 0) {
2167: if (rows[i] >= A->cmap->n) {
2168: a->ilen[rows[i]] = 0;
2169: } else {
2170: a->ilen[rows[i]] = 1;
2171: aa[a->i[rows[i]]] = diag;
2172: a->j[a->i[rows[i]]] = rows[i];
2173: }
2174: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2175: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2176: }
2177: }
2178: } else {
2179: for (i = 0; i < N; i++) {
2180: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2181: a->ilen[rows[i]] = 0;
2182: }
2183: }
2184: A->nonzerostate++;
2185: }
2186: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2187: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2188: PetscFunctionReturn(PETSC_SUCCESS);
2189: }
2191: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2192: {
2193: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2194: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2195: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2196: const PetscScalar *xx;
2197: PetscScalar *bb, *aa;
2199: PetscFunctionBegin;
2200: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2201: PetscCall(MatSeqAIJGetArray(A, &aa));
2202: if (x && b) {
2203: PetscCall(VecGetArrayRead(x, &xx));
2204: PetscCall(VecGetArray(b, &bb));
2205: vecs = PETSC_TRUE;
2206: }
2207: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2208: for (i = 0; i < N; i++) {
2209: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2210: PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));
2212: zeroed[rows[i]] = PETSC_TRUE;
2213: }
2214: for (i = 0; i < A->rmap->n; i++) {
2215: if (!zeroed[i]) {
2216: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2217: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2218: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2219: aa[j] = 0.0;
2220: }
2221: }
2222: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2223: }
2224: if (x && b) {
2225: PetscCall(VecRestoreArrayRead(x, &xx));
2226: PetscCall(VecRestoreArray(b, &bb));
2227: }
2228: PetscCall(PetscFree(zeroed));
2229: if (diag != 0.0) {
2230: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2231: if (missing) {
2232: for (i = 0; i < N; i++) {
2233: if (rows[i] >= A->cmap->N) continue;
2234: 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]);
2235: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2236: }
2237: } else {
2238: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2239: }
2240: }
2241: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2242: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2243: PetscFunctionReturn(PETSC_SUCCESS);
2244: }
2246: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2247: {
2248: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2249: const PetscScalar *aa;
2251: PetscFunctionBegin;
2252: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2253: *nz = a->i[row + 1] - a->i[row];
2254: if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2255: if (idx) {
2256: if (*nz && a->j) *idx = a->j + a->i[row];
2257: else *idx = NULL;
2258: }
2259: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2260: PetscFunctionReturn(PETSC_SUCCESS);
2261: }
2263: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2264: {
2265: PetscFunctionBegin;
2266: PetscFunctionReturn(PETSC_SUCCESS);
2267: }
2269: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2270: {
2271: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2272: const MatScalar *v;
2273: PetscReal sum = 0.0;
2274: PetscInt i, j;
2276: PetscFunctionBegin;
2277: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2278: if (type == NORM_FROBENIUS) {
2279: #if defined(PETSC_USE_REAL___FP16)
2280: PetscBLASInt one = 1, nz = a->nz;
2281: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2282: #else
2283: for (i = 0; i < a->nz; i++) {
2284: sum += PetscRealPart(PetscConj(*v) * (*v));
2285: v++;
2286: }
2287: *nrm = PetscSqrtReal(sum);
2288: #endif
2289: PetscCall(PetscLogFlops(2.0 * a->nz));
2290: } else if (type == NORM_1) {
2291: PetscReal *tmp;
2292: PetscInt *jj = a->j;
2293: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2294: *nrm = 0.0;
2295: for (j = 0; j < a->nz; j++) {
2296: tmp[*jj++] += PetscAbsScalar(*v);
2297: v++;
2298: }
2299: for (j = 0; j < A->cmap->n; j++) {
2300: if (tmp[j] > *nrm) *nrm = tmp[j];
2301: }
2302: PetscCall(PetscFree(tmp));
2303: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2304: } else if (type == NORM_INFINITY) {
2305: *nrm = 0.0;
2306: for (j = 0; j < A->rmap->n; j++) {
2307: const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2308: sum = 0.0;
2309: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2310: sum += PetscAbsScalar(*v2);
2311: v2++;
2312: }
2313: if (sum > *nrm) *nrm = sum;
2314: }
2315: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2316: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2317: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2318: PetscFunctionReturn(PETSC_SUCCESS);
2319: }
2321: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2322: {
2323: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2324: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2325: const MatScalar *va, *vb;
2326: PetscInt ma, na, mb, nb, i;
2328: PetscFunctionBegin;
2329: PetscCall(MatGetSize(A, &ma, &na));
2330: PetscCall(MatGetSize(B, &mb, &nb));
2331: if (ma != nb || na != mb) {
2332: *f = PETSC_FALSE;
2333: PetscFunctionReturn(PETSC_SUCCESS);
2334: }
2335: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2336: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2337: aii = aij->i;
2338: bii = bij->i;
2339: adx = aij->j;
2340: bdx = bij->j;
2341: PetscCall(PetscMalloc1(ma, &aptr));
2342: PetscCall(PetscMalloc1(mb, &bptr));
2343: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2344: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2346: *f = PETSC_TRUE;
2347: for (i = 0; i < ma; i++) {
2348: while (aptr[i] < aii[i + 1]) {
2349: PetscInt idc, idr;
2350: PetscScalar vc, vr;
2351: /* column/row index/value */
2352: idc = adx[aptr[i]];
2353: idr = bdx[bptr[idc]];
2354: vc = va[aptr[i]];
2355: vr = vb[bptr[idc]];
2356: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2357: *f = PETSC_FALSE;
2358: goto done;
2359: } else {
2360: aptr[i]++;
2361: if (B || i != idc) bptr[idc]++;
2362: }
2363: }
2364: }
2365: done:
2366: PetscCall(PetscFree(aptr));
2367: PetscCall(PetscFree(bptr));
2368: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2369: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2370: PetscFunctionReturn(PETSC_SUCCESS);
2371: }
2373: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2374: {
2375: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2376: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2377: MatScalar *va, *vb;
2378: PetscInt ma, na, mb, nb, i;
2380: PetscFunctionBegin;
2381: PetscCall(MatGetSize(A, &ma, &na));
2382: PetscCall(MatGetSize(B, &mb, &nb));
2383: if (ma != nb || na != mb) {
2384: *f = PETSC_FALSE;
2385: PetscFunctionReturn(PETSC_SUCCESS);
2386: }
2387: aii = aij->i;
2388: bii = bij->i;
2389: adx = aij->j;
2390: bdx = bij->j;
2391: va = aij->a;
2392: vb = bij->a;
2393: PetscCall(PetscMalloc1(ma, &aptr));
2394: PetscCall(PetscMalloc1(mb, &bptr));
2395: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2396: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2398: *f = PETSC_TRUE;
2399: for (i = 0; i < ma; i++) {
2400: while (aptr[i] < aii[i + 1]) {
2401: PetscInt idc, idr;
2402: PetscScalar vc, vr;
2403: /* column/row index/value */
2404: idc = adx[aptr[i]];
2405: idr = bdx[bptr[idc]];
2406: vc = va[aptr[i]];
2407: vr = vb[bptr[idc]];
2408: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2409: *f = PETSC_FALSE;
2410: goto done;
2411: } else {
2412: aptr[i]++;
2413: if (B || i != idc) bptr[idc]++;
2414: }
2415: }
2416: }
2417: done:
2418: PetscCall(PetscFree(aptr));
2419: PetscCall(PetscFree(bptr));
2420: PetscFunctionReturn(PETSC_SUCCESS);
2421: }
2423: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2424: {
2425: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2426: const PetscScalar *l, *r;
2427: PetscScalar x;
2428: MatScalar *v;
2429: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2430: const PetscInt *jj;
2432: PetscFunctionBegin;
2433: if (ll) {
2434: /* The local size is used so that VecMPI can be passed to this routine
2435: by MatDiagonalScale_MPIAIJ */
2436: PetscCall(VecGetLocalSize(ll, &m));
2437: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2438: PetscCall(VecGetArrayRead(ll, &l));
2439: PetscCall(MatSeqAIJGetArray(A, &v));
2440: for (i = 0; i < m; i++) {
2441: x = l[i];
2442: M = a->i[i + 1] - a->i[i];
2443: for (j = 0; j < M; j++) (*v++) *= x;
2444: }
2445: PetscCall(VecRestoreArrayRead(ll, &l));
2446: PetscCall(PetscLogFlops(nz));
2447: PetscCall(MatSeqAIJRestoreArray(A, &v));
2448: }
2449: if (rr) {
2450: PetscCall(VecGetLocalSize(rr, &n));
2451: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2452: PetscCall(VecGetArrayRead(rr, &r));
2453: PetscCall(MatSeqAIJGetArray(A, &v));
2454: jj = a->j;
2455: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2456: PetscCall(MatSeqAIJRestoreArray(A, &v));
2457: PetscCall(VecRestoreArrayRead(rr, &r));
2458: PetscCall(PetscLogFlops(nz));
2459: }
2460: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2461: PetscFunctionReturn(PETSC_SUCCESS);
2462: }
2464: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2465: {
2466: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2467: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2468: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2469: const PetscInt *irow, *icol;
2470: const PetscScalar *aa;
2471: PetscInt nrows, ncols;
2472: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2473: MatScalar *a_new, *mat_a, *c_a;
2474: Mat C;
2475: PetscBool stride;
2477: PetscFunctionBegin;
2478: PetscCall(ISGetIndices(isrow, &irow));
2479: PetscCall(ISGetLocalSize(isrow, &nrows));
2480: PetscCall(ISGetLocalSize(iscol, &ncols));
2482: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2483: if (stride) {
2484: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2485: } else {
2486: first = 0;
2487: step = 0;
2488: }
2489: if (stride && step == 1) {
2490: /* special case of contiguous rows */
2491: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2492: /* loop over new rows determining lens and starting points */
2493: for (i = 0; i < nrows; i++) {
2494: kstart = ai[irow[i]];
2495: kend = kstart + ailen[irow[i]];
2496: starts[i] = kstart;
2497: for (k = kstart; k < kend; k++) {
2498: if (aj[k] >= first) {
2499: starts[i] = k;
2500: break;
2501: }
2502: }
2503: sum = 0;
2504: while (k < kend) {
2505: if (aj[k++] >= first + ncols) break;
2506: sum++;
2507: }
2508: lens[i] = sum;
2509: }
2510: /* create submatrix */
2511: if (scall == MAT_REUSE_MATRIX) {
2512: PetscInt n_cols, n_rows;
2513: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2514: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2515: PetscCall(MatZeroEntries(*B));
2516: C = *B;
2517: } else {
2518: PetscInt rbs, cbs;
2519: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2520: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2521: PetscCall(ISGetBlockSize(isrow, &rbs));
2522: PetscCall(ISGetBlockSize(iscol, &cbs));
2523: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2524: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2525: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2526: }
2527: c = (Mat_SeqAIJ *)C->data;
2529: /* loop over rows inserting into submatrix */
2530: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2531: j_new = c->j;
2532: i_new = c->i;
2533: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2534: for (i = 0; i < nrows; i++) {
2535: ii = starts[i];
2536: lensi = lens[i];
2537: if (lensi) {
2538: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2539: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2540: a_new += lensi;
2541: }
2542: i_new[i + 1] = i_new[i] + lensi;
2543: c->ilen[i] = lensi;
2544: }
2545: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2546: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2547: PetscCall(PetscFree2(lens, starts));
2548: } else {
2549: PetscCall(ISGetIndices(iscol, &icol));
2550: PetscCall(PetscCalloc1(oldcols, &smap));
2551: PetscCall(PetscMalloc1(1 + nrows, &lens));
2552: for (i = 0; i < ncols; i++) {
2553: 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);
2554: smap[icol[i]] = i + 1;
2555: }
2557: /* determine lens of each row */
2558: for (i = 0; i < nrows; i++) {
2559: kstart = ai[irow[i]];
2560: kend = kstart + a->ilen[irow[i]];
2561: lens[i] = 0;
2562: for (k = kstart; k < kend; k++) {
2563: if (smap[aj[k]]) lens[i]++;
2564: }
2565: }
2566: /* Create and fill new matrix */
2567: if (scall == MAT_REUSE_MATRIX) {
2568: PetscBool equal;
2570: c = (Mat_SeqAIJ *)((*B)->data);
2571: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2572: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2573: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2574: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2575: C = *B;
2576: } else {
2577: PetscInt rbs, cbs;
2578: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2579: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2580: PetscCall(ISGetBlockSize(isrow, &rbs));
2581: PetscCall(ISGetBlockSize(iscol, &cbs));
2582: if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2583: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2584: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2585: }
2586: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2588: c = (Mat_SeqAIJ *)C->data;
2589: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2590: for (i = 0; i < nrows; i++) {
2591: row = irow[i];
2592: kstart = ai[row];
2593: kend = kstart + a->ilen[row];
2594: mat_i = c->i[i];
2595: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2596: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2597: mat_ilen = c->ilen + i;
2598: for (k = kstart; k < kend; k++) {
2599: if ((tcol = smap[a->j[k]])) {
2600: *mat_j++ = tcol - 1;
2601: *mat_a++ = aa[k];
2602: (*mat_ilen)++;
2603: }
2604: }
2605: }
2606: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2607: /* Free work space */
2608: PetscCall(ISRestoreIndices(iscol, &icol));
2609: PetscCall(PetscFree(smap));
2610: PetscCall(PetscFree(lens));
2611: /* sort */
2612: for (i = 0; i < nrows; i++) {
2613: PetscInt ilen;
2615: mat_i = c->i[i];
2616: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2617: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2618: ilen = c->ilen[i];
2619: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2620: }
2621: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2622: }
2623: #if defined(PETSC_HAVE_DEVICE)
2624: PetscCall(MatBindToCPU(C, A->boundtocpu));
2625: #endif
2626: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2627: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2629: PetscCall(ISRestoreIndices(isrow, &irow));
2630: *B = C;
2631: PetscFunctionReturn(PETSC_SUCCESS);
2632: }
2634: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2635: {
2636: Mat B;
2638: PetscFunctionBegin;
2639: if (scall == MAT_INITIAL_MATRIX) {
2640: PetscCall(MatCreate(subComm, &B));
2641: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2642: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2643: PetscCall(MatSetType(B, MATSEQAIJ));
2644: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2645: *subMat = B;
2646: } else {
2647: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2648: }
2649: PetscFunctionReturn(PETSC_SUCCESS);
2650: }
2652: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2653: {
2654: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2655: Mat outA;
2656: PetscBool row_identity, col_identity;
2658: PetscFunctionBegin;
2659: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2661: PetscCall(ISIdentity(row, &row_identity));
2662: PetscCall(ISIdentity(col, &col_identity));
2664: outA = inA;
2665: outA->factortype = MAT_FACTOR_LU;
2666: PetscCall(PetscFree(inA->solvertype));
2667: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2669: PetscCall(PetscObjectReference((PetscObject)row));
2670: PetscCall(ISDestroy(&a->row));
2672: a->row = row;
2674: PetscCall(PetscObjectReference((PetscObject)col));
2675: PetscCall(ISDestroy(&a->col));
2677: a->col = col;
2679: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2680: PetscCall(ISDestroy(&a->icol));
2681: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2683: if (!a->solve_work) { /* this matrix may have been factored before */
2684: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2685: }
2687: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2688: if (row_identity && col_identity) {
2689: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2690: } else {
2691: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2692: }
2693: PetscFunctionReturn(PETSC_SUCCESS);
2694: }
2696: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2697: {
2698: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2699: PetscScalar *v;
2700: PetscBLASInt one = 1, bnz;
2702: PetscFunctionBegin;
2703: PetscCall(MatSeqAIJGetArray(inA, &v));
2704: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2705: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2706: PetscCall(PetscLogFlops(a->nz));
2707: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2708: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2709: PetscFunctionReturn(PETSC_SUCCESS);
2710: }
2712: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2713: {
2714: PetscInt i;
2716: PetscFunctionBegin;
2717: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2718: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2720: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2721: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2723: if (submatj->rbuf1) {
2724: PetscCall(PetscFree(submatj->rbuf1[0]));
2725: PetscCall(PetscFree(submatj->rbuf1));
2726: }
2728: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2729: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2730: PetscCall(PetscFree(submatj->pa));
2731: }
2733: #if defined(PETSC_USE_CTABLE)
2734: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2735: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2736: PetscCall(PetscFree(submatj->rmap_loc));
2737: #else
2738: PetscCall(PetscFree(submatj->rmap));
2739: #endif
2741: if (!submatj->allcolumns) {
2742: #if defined(PETSC_USE_CTABLE)
2743: PetscCall(PetscHMapIDestroy(&submatj->cmap));
2744: #else
2745: PetscCall(PetscFree(submatj->cmap));
2746: #endif
2747: }
2748: PetscCall(PetscFree(submatj->row2proc));
2750: PetscCall(PetscFree(submatj));
2751: PetscFunctionReturn(PETSC_SUCCESS);
2752: }
2754: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2755: {
2756: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2757: Mat_SubSppt *submatj = c->submatis1;
2759: PetscFunctionBegin;
2760: PetscCall((*submatj->destroy)(C));
2761: PetscCall(MatDestroySubMatrix_Private(submatj));
2762: PetscFunctionReturn(PETSC_SUCCESS);
2763: }
2765: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2766: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2767: {
2768: PetscInt i;
2769: Mat C;
2770: Mat_SeqAIJ *c;
2771: Mat_SubSppt *submatj;
2773: PetscFunctionBegin;
2774: for (i = 0; i < n; i++) {
2775: C = (*mat)[i];
2776: c = (Mat_SeqAIJ *)C->data;
2777: submatj = c->submatis1;
2778: if (submatj) {
2779: if (--((PetscObject)C)->refct <= 0) {
2780: PetscCall(PetscFree(C->factorprefix));
2781: PetscCall((*submatj->destroy)(C));
2782: PetscCall(MatDestroySubMatrix_Private(submatj));
2783: PetscCall(PetscFree(C->defaultvectype));
2784: PetscCall(PetscFree(C->defaultrandtype));
2785: PetscCall(PetscLayoutDestroy(&C->rmap));
2786: PetscCall(PetscLayoutDestroy(&C->cmap));
2787: PetscCall(PetscHeaderDestroy(&C));
2788: }
2789: } else {
2790: PetscCall(MatDestroy(&C));
2791: }
2792: }
2794: /* Destroy Dummy submatrices created for reuse */
2795: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2797: PetscCall(PetscFree(*mat));
2798: PetscFunctionReturn(PETSC_SUCCESS);
2799: }
2801: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2802: {
2803: PetscInt i;
2805: PetscFunctionBegin;
2806: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2808: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2809: PetscFunctionReturn(PETSC_SUCCESS);
2810: }
2812: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2813: {
2814: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2815: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2816: const PetscInt *idx;
2817: PetscInt start, end, *ai, *aj, bs = A->rmap->bs == A->cmap->bs ? A->rmap->bs : 1;
2818: PetscBT table;
2820: PetscFunctionBegin;
2821: m = A->rmap->n / bs;
2822: ai = a->i;
2823: aj = a->j;
2825: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2827: PetscCall(PetscMalloc1(m + 1, &nidx));
2828: PetscCall(PetscBTCreate(m, &table));
2830: for (i = 0; i < is_max; i++) {
2831: /* Initialize the two local arrays */
2832: isz = 0;
2833: PetscCall(PetscBTMemzero(m, table));
2835: /* Extract the indices, assume there can be duplicate entries */
2836: PetscCall(ISGetIndices(is[i], &idx));
2837: PetscCall(ISGetLocalSize(is[i], &n));
2839: if (bs > 1) {
2840: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2841: for (j = 0; j < n; ++j) {
2842: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2843: }
2844: PetscCall(ISRestoreIndices(is[i], &idx));
2845: PetscCall(ISDestroy(&is[i]));
2847: k = 0;
2848: for (j = 0; j < ov; j++) { /* for each overlap */
2849: n = isz;
2850: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2851: for (ll = 0; ll < bs; ll++) {
2852: row = bs * nidx[k] + ll;
2853: start = ai[row];
2854: end = ai[row + 1];
2855: for (l = start; l < end; l++) {
2856: val = aj[l] / bs;
2857: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2858: }
2859: }
2860: }
2861: }
2862: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, is + i));
2863: } else {
2864: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2865: for (j = 0; j < n; ++j) {
2866: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2867: }
2868: PetscCall(ISRestoreIndices(is[i], &idx));
2869: PetscCall(ISDestroy(&is[i]));
2871: k = 0;
2872: for (j = 0; j < ov; j++) { /* for each overlap */
2873: n = isz;
2874: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2875: row = nidx[k];
2876: start = ai[row];
2877: end = ai[row + 1];
2878: for (l = start; l < end; l++) {
2879: val = aj[l];
2880: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2881: }
2882: }
2883: }
2884: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, is + i));
2885: }
2886: }
2887: PetscCall(PetscBTDestroy(&table));
2888: PetscCall(PetscFree(nidx));
2889: PetscFunctionReturn(PETSC_SUCCESS);
2890: }
2892: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2893: {
2894: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2895: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2896: const PetscInt *row, *col;
2897: PetscInt *cnew, j, *lens;
2898: IS icolp, irowp;
2899: PetscInt *cwork = NULL;
2900: PetscScalar *vwork = NULL;
2902: PetscFunctionBegin;
2903: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2904: PetscCall(ISGetIndices(irowp, &row));
2905: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2906: PetscCall(ISGetIndices(icolp, &col));
2908: /* determine lengths of permuted rows */
2909: PetscCall(PetscMalloc1(m + 1, &lens));
2910: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2911: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2912: PetscCall(MatSetSizes(*B, m, n, m, n));
2913: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2914: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2915: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2916: PetscCall(PetscFree(lens));
2918: PetscCall(PetscMalloc1(n, &cnew));
2919: for (i = 0; i < m; i++) {
2920: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2921: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2922: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2923: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2924: }
2925: PetscCall(PetscFree(cnew));
2927: (*B)->assembled = PETSC_FALSE;
2929: #if defined(PETSC_HAVE_DEVICE)
2930: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2931: #endif
2932: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2933: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2934: PetscCall(ISRestoreIndices(irowp, &row));
2935: PetscCall(ISRestoreIndices(icolp, &col));
2936: PetscCall(ISDestroy(&irowp));
2937: PetscCall(ISDestroy(&icolp));
2938: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2939: PetscFunctionReturn(PETSC_SUCCESS);
2940: }
2942: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2943: {
2944: PetscFunctionBegin;
2945: /* If the two matrices have the same copy implementation, use fast copy. */
2946: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2947: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2948: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2949: const PetscScalar *aa;
2951: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2952: 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]);
2953: PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2954: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2955: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2956: } else {
2957: PetscCall(MatCopy_Basic(A, B, str));
2958: }
2959: PetscFunctionReturn(PETSC_SUCCESS);
2960: }
2962: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2963: {
2964: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2966: PetscFunctionBegin;
2967: *array = a->a;
2968: PetscFunctionReturn(PETSC_SUCCESS);
2969: }
2971: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2972: {
2973: PetscFunctionBegin;
2974: *array = NULL;
2975: PetscFunctionReturn(PETSC_SUCCESS);
2976: }
2978: /*
2979: Computes the number of nonzeros per row needed for preallocation when X and Y
2980: have different nonzero structure.
2981: */
2982: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2983: {
2984: PetscInt i, j, k, nzx, nzy;
2986: PetscFunctionBegin;
2987: /* Set the number of nonzeros in the new matrix */
2988: for (i = 0; i < m; i++) {
2989: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2990: nzx = xi[i + 1] - xi[i];
2991: nzy = yi[i + 1] - yi[i];
2992: nnz[i] = 0;
2993: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2994: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
2995: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
2996: nnz[i]++;
2997: }
2998: for (; k < nzy; k++) nnz[i]++;
2999: }
3000: PetscFunctionReturn(PETSC_SUCCESS);
3001: }
3003: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3004: {
3005: PetscInt m = Y->rmap->N;
3006: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3007: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3009: PetscFunctionBegin;
3010: /* Set the number of nonzeros in the new matrix */
3011: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3012: PetscFunctionReturn(PETSC_SUCCESS);
3013: }
3015: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3016: {
3017: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3019: PetscFunctionBegin;
3020: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3021: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3022: if (e) {
3023: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3024: if (e) {
3025: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3026: if (e) str = SAME_NONZERO_PATTERN;
3027: }
3028: }
3029: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3030: }
3031: if (str == SAME_NONZERO_PATTERN) {
3032: const PetscScalar *xa;
3033: PetscScalar *ya, alpha = a;
3034: PetscBLASInt one = 1, bnz;
3036: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3037: PetscCall(MatSeqAIJGetArray(Y, &ya));
3038: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3039: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3040: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3041: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3042: PetscCall(PetscLogFlops(2.0 * bnz));
3043: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3044: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3045: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3046: PetscCall(MatAXPY_Basic(Y, a, X, str));
3047: } else {
3048: Mat B;
3049: PetscInt *nnz;
3050: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3051: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3052: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3053: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3054: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3055: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3056: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3057: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3058: PetscCall(MatHeaderMerge(Y, &B));
3059: PetscCall(MatSeqAIJCheckInode(Y));
3060: PetscCall(PetscFree(nnz));
3061: }
3062: PetscFunctionReturn(PETSC_SUCCESS);
3063: }
3065: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3066: {
3067: #if defined(PETSC_USE_COMPLEX)
3068: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3069: PetscInt i, nz;
3070: PetscScalar *a;
3072: PetscFunctionBegin;
3073: nz = aij->nz;
3074: PetscCall(MatSeqAIJGetArray(mat, &a));
3075: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3076: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3077: #else
3078: PetscFunctionBegin;
3079: #endif
3080: PetscFunctionReturn(PETSC_SUCCESS);
3081: }
3083: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3084: {
3085: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3086: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3087: PetscReal atmp;
3088: PetscScalar *x;
3089: const MatScalar *aa, *av;
3091: PetscFunctionBegin;
3092: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3093: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3094: aa = av;
3095: ai = a->i;
3096: aj = a->j;
3098: PetscCall(VecGetArrayWrite(v, &x));
3099: PetscCall(VecGetLocalSize(v, &n));
3100: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3101: for (i = 0; i < m; i++) {
3102: ncols = ai[1] - ai[0];
3103: ai++;
3104: x[i] = 0;
3105: for (j = 0; j < ncols; j++) {
3106: atmp = PetscAbsScalar(*aa);
3107: if (PetscAbsScalar(x[i]) < atmp) {
3108: x[i] = atmp;
3109: if (idx) idx[i] = *aj;
3110: }
3111: aa++;
3112: aj++;
3113: }
3114: }
3115: PetscCall(VecRestoreArrayWrite(v, &x));
3116: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3117: PetscFunctionReturn(PETSC_SUCCESS);
3118: }
3120: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3121: {
3122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3123: PetscInt i, j, m = A->rmap->n, *ai, ncols, n;
3124: PetscScalar *x;
3125: const MatScalar *aa, *av;
3127: PetscFunctionBegin;
3128: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3129: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3130: aa = av;
3131: ai = a->i;
3133: PetscCall(VecGetArrayWrite(v, &x));
3134: PetscCall(VecGetLocalSize(v, &n));
3135: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3136: for (i = 0; i < m; i++) {
3137: ncols = ai[1] - ai[0];
3138: ai++;
3139: x[i] = 0;
3140: for (j = 0; j < ncols; j++) {
3141: x[i] += PetscAbsScalar(*aa);
3142: aa++;
3143: }
3144: }
3145: PetscCall(VecRestoreArrayWrite(v, &x));
3146: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3147: PetscFunctionReturn(PETSC_SUCCESS);
3148: }
3150: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3151: {
3152: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3153: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3154: PetscScalar *x;
3155: const MatScalar *aa, *av;
3157: PetscFunctionBegin;
3158: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3159: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3160: aa = av;
3161: ai = a->i;
3162: aj = a->j;
3164: PetscCall(VecGetArrayWrite(v, &x));
3165: PetscCall(VecGetLocalSize(v, &n));
3166: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3167: for (i = 0; i < m; i++) {
3168: ncols = ai[1] - ai[0];
3169: ai++;
3170: if (ncols == A->cmap->n) { /* row is dense */
3171: x[i] = *aa;
3172: if (idx) idx[i] = 0;
3173: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3174: x[i] = 0.0;
3175: if (idx) {
3176: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3177: if (aj[j] > j) {
3178: idx[i] = j;
3179: break;
3180: }
3181: }
3182: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3183: if (j == ncols && j < A->cmap->n) idx[i] = j;
3184: }
3185: }
3186: for (j = 0; j < ncols; j++) {
3187: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3188: x[i] = *aa;
3189: if (idx) idx[i] = *aj;
3190: }
3191: aa++;
3192: aj++;
3193: }
3194: }
3195: PetscCall(VecRestoreArrayWrite(v, &x));
3196: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3197: PetscFunctionReturn(PETSC_SUCCESS);
3198: }
3200: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3201: {
3202: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3203: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3204: PetscScalar *x;
3205: const MatScalar *aa, *av;
3207: PetscFunctionBegin;
3208: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3209: aa = av;
3210: ai = a->i;
3211: aj = a->j;
3213: PetscCall(VecGetArrayWrite(v, &x));
3214: PetscCall(VecGetLocalSize(v, &n));
3215: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3216: for (i = 0; i < m; i++) {
3217: ncols = ai[1] - ai[0];
3218: ai++;
3219: if (ncols == A->cmap->n) { /* row is dense */
3220: x[i] = *aa;
3221: if (idx) idx[i] = 0;
3222: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3223: x[i] = 0.0;
3224: if (idx) { /* find first implicit 0.0 in the row */
3225: for (j = 0; j < ncols; j++) {
3226: if (aj[j] > j) {
3227: idx[i] = j;
3228: break;
3229: }
3230: }
3231: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3232: if (j == ncols && j < A->cmap->n) idx[i] = j;
3233: }
3234: }
3235: for (j = 0; j < ncols; j++) {
3236: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3237: x[i] = *aa;
3238: if (idx) idx[i] = *aj;
3239: }
3240: aa++;
3241: aj++;
3242: }
3243: }
3244: PetscCall(VecRestoreArrayWrite(v, &x));
3245: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3246: PetscFunctionReturn(PETSC_SUCCESS);
3247: }
3249: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3250: {
3251: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3252: PetscInt i, j, m = A->rmap->n, ncols, n;
3253: const PetscInt *ai, *aj;
3254: PetscScalar *x;
3255: const MatScalar *aa, *av;
3257: PetscFunctionBegin;
3258: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3259: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3260: aa = av;
3261: ai = a->i;
3262: aj = a->j;
3264: PetscCall(VecGetArrayWrite(v, &x));
3265: PetscCall(VecGetLocalSize(v, &n));
3266: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3267: for (i = 0; i < m; i++) {
3268: ncols = ai[1] - ai[0];
3269: ai++;
3270: if (ncols == A->cmap->n) { /* row is dense */
3271: x[i] = *aa;
3272: if (idx) idx[i] = 0;
3273: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3274: x[i] = 0.0;
3275: if (idx) { /* find first implicit 0.0 in the row */
3276: for (j = 0; j < ncols; j++) {
3277: if (aj[j] > j) {
3278: idx[i] = j;
3279: break;
3280: }
3281: }
3282: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3283: if (j == ncols && j < A->cmap->n) idx[i] = j;
3284: }
3285: }
3286: for (j = 0; j < ncols; j++) {
3287: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3288: x[i] = *aa;
3289: if (idx) idx[i] = *aj;
3290: }
3291: aa++;
3292: aj++;
3293: }
3294: }
3295: PetscCall(VecRestoreArrayWrite(v, &x));
3296: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3297: PetscFunctionReturn(PETSC_SUCCESS);
3298: }
3300: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3301: {
3302: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3303: PetscInt i, bs = A->rmap->bs, mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3304: MatScalar *diag, work[25], *v_work;
3305: const PetscReal shift = 0.0;
3306: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3308: PetscFunctionBegin;
3309: allowzeropivot = PetscNot(A->erroriffailure);
3310: if (a->ibdiagvalid) {
3311: if (values) *values = a->ibdiag;
3312: PetscFunctionReturn(PETSC_SUCCESS);
3313: }
3314: PetscCall(MatMarkDiagonal_SeqAIJ(A));
3315: if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3316: diag = a->ibdiag;
3317: if (values) *values = a->ibdiag;
3318: /* factor and invert each block */
3319: switch (bs) {
3320: case 1:
3321: for (i = 0; i < mbs; i++) {
3322: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3323: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3324: if (allowzeropivot) {
3325: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3326: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3327: A->factorerror_zeropivot_row = i;
3328: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3329: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON);
3330: }
3331: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3332: }
3333: break;
3334: case 2:
3335: for (i = 0; i < mbs; i++) {
3336: ij[0] = 2 * i;
3337: ij[1] = 2 * i + 1;
3338: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3339: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3340: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3341: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3342: diag += 4;
3343: }
3344: break;
3345: case 3:
3346: for (i = 0; i < mbs; i++) {
3347: ij[0] = 3 * i;
3348: ij[1] = 3 * i + 1;
3349: ij[2] = 3 * i + 2;
3350: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3351: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3352: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3353: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3354: diag += 9;
3355: }
3356: break;
3357: case 4:
3358: for (i = 0; i < mbs; i++) {
3359: ij[0] = 4 * i;
3360: ij[1] = 4 * i + 1;
3361: ij[2] = 4 * i + 2;
3362: ij[3] = 4 * i + 3;
3363: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3364: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3365: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3366: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3367: diag += 16;
3368: }
3369: break;
3370: case 5:
3371: for (i = 0; i < mbs; i++) {
3372: ij[0] = 5 * i;
3373: ij[1] = 5 * i + 1;
3374: ij[2] = 5 * i + 2;
3375: ij[3] = 5 * i + 3;
3376: ij[4] = 5 * i + 4;
3377: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3378: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3379: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3380: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3381: diag += 25;
3382: }
3383: break;
3384: case 6:
3385: for (i = 0; i < mbs; i++) {
3386: ij[0] = 6 * i;
3387: ij[1] = 6 * i + 1;
3388: ij[2] = 6 * i + 2;
3389: ij[3] = 6 * i + 3;
3390: ij[4] = 6 * i + 4;
3391: ij[5] = 6 * i + 5;
3392: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3393: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3394: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3395: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3396: diag += 36;
3397: }
3398: break;
3399: case 7:
3400: for (i = 0; i < mbs; i++) {
3401: ij[0] = 7 * i;
3402: ij[1] = 7 * i + 1;
3403: ij[2] = 7 * i + 2;
3404: ij[3] = 7 * i + 3;
3405: ij[4] = 7 * i + 4;
3406: ij[5] = 7 * i + 5;
3407: ij[6] = 7 * i + 6;
3408: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3409: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3410: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3411: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3412: diag += 49;
3413: }
3414: break;
3415: default:
3416: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3417: for (i = 0; i < mbs; i++) {
3418: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3419: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3420: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3421: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3422: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3423: diag += bs2;
3424: }
3425: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3426: }
3427: a->ibdiagvalid = PETSC_TRUE;
3428: PetscFunctionReturn(PETSC_SUCCESS);
3429: }
3431: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3432: {
3433: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3434: PetscScalar a, *aa;
3435: PetscInt m, n, i, j, col;
3437: PetscFunctionBegin;
3438: if (!x->assembled) {
3439: PetscCall(MatGetSize(x, &m, &n));
3440: for (i = 0; i < m; i++) {
3441: for (j = 0; j < aij->imax[i]; j++) {
3442: PetscCall(PetscRandomGetValue(rctx, &a));
3443: col = (PetscInt)(n * PetscRealPart(a));
3444: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3445: }
3446: }
3447: } else {
3448: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3449: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3450: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3451: }
3452: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3453: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3454: PetscFunctionReturn(PETSC_SUCCESS);
3455: }
3457: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3458: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3459: {
3460: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3461: PetscScalar a;
3462: PetscInt m, n, i, j, col, nskip;
3464: PetscFunctionBegin;
3465: nskip = high - low;
3466: PetscCall(MatGetSize(x, &m, &n));
3467: n -= nskip; /* shrink number of columns where nonzeros can be set */
3468: for (i = 0; i < m; i++) {
3469: for (j = 0; j < aij->imax[i]; j++) {
3470: PetscCall(PetscRandomGetValue(rctx, &a));
3471: col = (PetscInt)(n * PetscRealPart(a));
3472: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3473: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3474: }
3475: }
3476: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3477: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3478: PetscFunctionReturn(PETSC_SUCCESS);
3479: }
3481: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3482: MatGetRow_SeqAIJ,
3483: MatRestoreRow_SeqAIJ,
3484: MatMult_SeqAIJ,
3485: /* 4*/ MatMultAdd_SeqAIJ,
3486: MatMultTranspose_SeqAIJ,
3487: MatMultTransposeAdd_SeqAIJ,
3488: NULL,
3489: NULL,
3490: NULL,
3491: /* 10*/ NULL,
3492: MatLUFactor_SeqAIJ,
3493: NULL,
3494: MatSOR_SeqAIJ,
3495: MatTranspose_SeqAIJ,
3496: /*1 5*/ MatGetInfo_SeqAIJ,
3497: MatEqual_SeqAIJ,
3498: MatGetDiagonal_SeqAIJ,
3499: MatDiagonalScale_SeqAIJ,
3500: MatNorm_SeqAIJ,
3501: /* 20*/ NULL,
3502: MatAssemblyEnd_SeqAIJ,
3503: MatSetOption_SeqAIJ,
3504: MatZeroEntries_SeqAIJ,
3505: /* 24*/ MatZeroRows_SeqAIJ,
3506: NULL,
3507: NULL,
3508: NULL,
3509: NULL,
3510: /* 29*/ MatSetUp_Seq_Hash,
3511: NULL,
3512: NULL,
3513: NULL,
3514: NULL,
3515: /* 34*/ MatDuplicate_SeqAIJ,
3516: NULL,
3517: NULL,
3518: MatILUFactor_SeqAIJ,
3519: NULL,
3520: /* 39*/ MatAXPY_SeqAIJ,
3521: MatCreateSubMatrices_SeqAIJ,
3522: MatIncreaseOverlap_SeqAIJ,
3523: MatGetValues_SeqAIJ,
3524: MatCopy_SeqAIJ,
3525: /* 44*/ MatGetRowMax_SeqAIJ,
3526: MatScale_SeqAIJ,
3527: MatShift_SeqAIJ,
3528: MatDiagonalSet_SeqAIJ,
3529: MatZeroRowsColumns_SeqAIJ,
3530: /* 49*/ MatSetRandom_SeqAIJ,
3531: MatGetRowIJ_SeqAIJ,
3532: MatRestoreRowIJ_SeqAIJ,
3533: MatGetColumnIJ_SeqAIJ,
3534: MatRestoreColumnIJ_SeqAIJ,
3535: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3536: NULL,
3537: NULL,
3538: MatPermute_SeqAIJ,
3539: NULL,
3540: /* 59*/ NULL,
3541: MatDestroy_SeqAIJ,
3542: MatView_SeqAIJ,
3543: NULL,
3544: NULL,
3545: /* 64*/ NULL,
3546: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3547: NULL,
3548: NULL,
3549: NULL,
3550: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3551: MatGetRowMinAbs_SeqAIJ,
3552: NULL,
3553: NULL,
3554: NULL,
3555: /* 74*/ NULL,
3556: MatFDColoringApply_AIJ,
3557: NULL,
3558: NULL,
3559: NULL,
3560: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3561: NULL,
3562: NULL,
3563: NULL,
3564: MatLoad_SeqAIJ,
3565: /* 84*/ NULL,
3566: NULL,
3567: NULL,
3568: NULL,
3569: NULL,
3570: /* 89*/ NULL,
3571: NULL,
3572: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3573: NULL,
3574: NULL,
3575: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3576: NULL,
3577: NULL,
3578: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3579: NULL,
3580: /* 99*/ MatProductSetFromOptions_SeqAIJ,
3581: NULL,
3582: NULL,
3583: MatConjugate_SeqAIJ,
3584: NULL,
3585: /*104*/ MatSetValuesRow_SeqAIJ,
3586: MatRealPart_SeqAIJ,
3587: MatImaginaryPart_SeqAIJ,
3588: NULL,
3589: NULL,
3590: /*109*/ MatMatSolve_SeqAIJ,
3591: NULL,
3592: MatGetRowMin_SeqAIJ,
3593: NULL,
3594: MatMissingDiagonal_SeqAIJ,
3595: /*114*/ NULL,
3596: NULL,
3597: NULL,
3598: NULL,
3599: NULL,
3600: /*119*/ NULL,
3601: NULL,
3602: NULL,
3603: NULL,
3604: MatGetMultiProcBlock_SeqAIJ,
3605: /*124*/ MatFindNonzeroRows_SeqAIJ,
3606: MatGetColumnReductions_SeqAIJ,
3607: MatInvertBlockDiagonal_SeqAIJ,
3608: MatInvertVariableBlockDiagonal_SeqAIJ,
3609: NULL,
3610: /*129*/ NULL,
3611: NULL,
3612: NULL,
3613: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3614: MatTransposeColoringCreate_SeqAIJ,
3615: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3616: MatTransColoringApplyDenToSp_SeqAIJ,
3617: NULL,
3618: NULL,
3619: MatRARtNumeric_SeqAIJ_SeqAIJ,
3620: /*139*/ NULL,
3621: NULL,
3622: NULL,
3623: MatFDColoringSetUp_SeqXAIJ,
3624: MatFindOffBlockDiagonalEntries_SeqAIJ,
3625: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3626: /*145*/ MatDestroySubMatrices_SeqAIJ,
3627: NULL,
3628: NULL,
3629: MatCreateGraph_Simple_AIJ,
3630: NULL,
3631: /*150*/ MatTransposeSymbolic_SeqAIJ,
3632: MatEliminateZeros_SeqAIJ,
3633: MatGetRowSumAbs_SeqAIJ,
3634: NULL,
3635: NULL,
3636: /*155*/ NULL,
3637: MatCopyHashToXAIJ_Seq_Hash};
3639: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3640: {
3641: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3642: PetscInt i, nz, n;
3644: PetscFunctionBegin;
3645: nz = aij->maxnz;
3646: n = mat->rmap->n;
3647: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3648: aij->nz = nz;
3649: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3650: PetscFunctionReturn(PETSC_SUCCESS);
3651: }
3653: /*
3654: * Given a sparse matrix with global column indices, compact it by using a local column space.
3655: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3656: */
3657: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3658: {
3659: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3660: PetscHMapI gid1_lid1;
3661: PetscHashIter tpos;
3662: PetscInt gid, lid, i, ec, nz = aij->nz;
3663: PetscInt *garray, *jj = aij->j;
3665: PetscFunctionBegin;
3667: PetscAssertPointer(mapping, 2);
3668: /* use a table */
3669: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3670: ec = 0;
3671: for (i = 0; i < nz; i++) {
3672: PetscInt data, gid1 = jj[i] + 1;
3673: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3674: if (!data) {
3675: /* one based table */
3676: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3677: }
3678: }
3679: /* form array of columns we need */
3680: PetscCall(PetscMalloc1(ec, &garray));
3681: PetscHashIterBegin(gid1_lid1, tpos);
3682: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3683: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3684: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3685: PetscHashIterNext(gid1_lid1, tpos);
3686: gid--;
3687: lid--;
3688: garray[lid] = gid;
3689: }
3690: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3691: PetscCall(PetscHMapIClear(gid1_lid1));
3692: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3693: /* compact out the extra columns in B */
3694: for (i = 0; i < nz; i++) {
3695: PetscInt gid1 = jj[i] + 1;
3696: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3697: lid--;
3698: jj[i] = lid;
3699: }
3700: PetscCall(PetscLayoutDestroy(&mat->cmap));
3701: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3702: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3703: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3704: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3705: PetscFunctionReturn(PETSC_SUCCESS);
3706: }
3708: /*@
3709: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3710: in the matrix.
3712: Input Parameters:
3713: + mat - the `MATSEQAIJ` matrix
3714: - indices - the column indices
3716: Level: advanced
3718: Notes:
3719: This can be called if you have precomputed the nonzero structure of the
3720: matrix and want to provide it to the matrix object to improve the performance
3721: of the `MatSetValues()` operation.
3723: You MUST have set the correct numbers of nonzeros per row in the call to
3724: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3726: MUST be called before any calls to `MatSetValues()`
3728: The indices should start with zero, not one.
3730: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3731: @*/
3732: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3733: {
3734: PetscFunctionBegin;
3736: PetscAssertPointer(indices, 2);
3737: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3738: PetscFunctionReturn(PETSC_SUCCESS);
3739: }
3741: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3742: {
3743: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3744: size_t nz = aij->i[mat->rmap->n];
3746: PetscFunctionBegin;
3747: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3749: /* allocate space for values if not already there */
3750: if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }
3752: /* copy values over */
3753: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3754: PetscFunctionReturn(PETSC_SUCCESS);
3755: }
3757: /*@
3758: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3759: nonlinear portion.
3761: Logically Collect
3763: Input Parameter:
3764: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3766: Level: advanced
3768: Example Usage:
3769: .vb
3770: Using SNES
3771: Create Jacobian matrix
3772: Set linear terms into matrix
3773: Apply boundary conditions to matrix, at this time matrix must have
3774: final nonzero structure (i.e. setting the nonlinear terms and applying
3775: boundary conditions again will not change the nonzero structure
3776: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3777: MatStoreValues(mat);
3778: Call SNESSetJacobian() with matrix
3779: In your Jacobian routine
3780: MatRetrieveValues(mat);
3781: Set nonlinear terms in matrix
3783: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3784: // build linear portion of Jacobian
3785: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3786: MatStoreValues(mat);
3787: loop over nonlinear iterations
3788: MatRetrieveValues(mat);
3789: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3790: // call MatAssemblyBegin/End() on matrix
3791: Solve linear system with Jacobian
3792: endloop
3793: .ve
3795: Notes:
3796: Matrix must already be assembled before calling this routine
3797: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3798: calling this routine.
3800: When this is called multiple times it overwrites the previous set of stored values
3801: and does not allocated additional space.
3803: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3804: @*/
3805: PetscErrorCode MatStoreValues(Mat mat)
3806: {
3807: PetscFunctionBegin;
3809: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3810: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3811: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3812: PetscFunctionReturn(PETSC_SUCCESS);
3813: }
3815: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3816: {
3817: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3818: PetscInt nz = aij->i[mat->rmap->n];
3820: PetscFunctionBegin;
3821: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3822: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3823: /* copy values over */
3824: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3825: PetscFunctionReturn(PETSC_SUCCESS);
3826: }
3828: /*@
3829: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3831: Logically Collect
3833: Input Parameter:
3834: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3836: Level: advanced
3838: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3839: @*/
3840: PetscErrorCode MatRetrieveValues(Mat mat)
3841: {
3842: PetscFunctionBegin;
3844: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3845: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3846: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3847: PetscFunctionReturn(PETSC_SUCCESS);
3848: }
3850: /*@
3851: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3852: (the default parallel PETSc format). For good matrix assembly performance
3853: the user should preallocate the matrix storage by setting the parameter `nz`
3854: (or the array `nnz`).
3856: Collective
3858: Input Parameters:
3859: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3860: . m - number of rows
3861: . n - number of columns
3862: . nz - number of nonzeros per row (same for all rows)
3863: - nnz - array containing the number of nonzeros in the various rows
3864: (possibly different for each row) or NULL
3866: Output Parameter:
3867: . A - the matrix
3869: Options Database Keys:
3870: + -mat_no_inode - Do not use inodes
3871: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3873: Level: intermediate
3875: Notes:
3876: It is recommend to use `MatCreateFromOptions()` instead of this routine
3878: If `nnz` is given then `nz` is ignored
3880: The `MATSEQAIJ` format, also called
3881: compressed row storage, is fully compatible with standard Fortran
3882: storage. That is, the stored row and column indices can begin at
3883: either one (as in Fortran) or zero.
3885: Specify the preallocated storage with either `nz` or `nnz` (not both).
3886: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3887: allocation.
3889: By default, this format uses inodes (identical nodes) when possible, to
3890: improve numerical efficiency of matrix-vector products and solves. We
3891: search for consecutive rows with the same nonzero structure, thereby
3892: reusing matrix information to achieve increased efficiency.
3894: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3895: @*/
3896: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3897: {
3898: PetscFunctionBegin;
3899: PetscCall(MatCreate(comm, A));
3900: PetscCall(MatSetSizes(*A, m, n, m, n));
3901: PetscCall(MatSetType(*A, MATSEQAIJ));
3902: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3903: PetscFunctionReturn(PETSC_SUCCESS);
3904: }
3906: /*@
3907: MatSeqAIJSetPreallocation - For good matrix assembly performance
3908: the user should preallocate the matrix storage by setting the parameter nz
3909: (or the array nnz). By setting these parameters accurately, performance
3910: during matrix assembly can be increased by more than a factor of 50.
3912: Collective
3914: Input Parameters:
3915: + B - The matrix
3916: . nz - number of nonzeros per row (same for all rows)
3917: - nnz - array containing the number of nonzeros in the various rows
3918: (possibly different for each row) or NULL
3920: Options Database Keys:
3921: + -mat_no_inode - Do not use inodes
3922: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3924: Level: intermediate
3926: Notes:
3927: If `nnz` is given then `nz` is ignored
3929: The `MATSEQAIJ` format also called
3930: compressed row storage, is fully compatible with standard Fortran
3931: storage. That is, the stored row and column indices can begin at
3932: either one (as in Fortran) or zero. See the users' manual for details.
3934: Specify the preallocated storage with either `nz` or `nnz` (not both).
3935: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3936: allocation.
3938: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3939: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3940: You can also run with the option -info and look for messages with the string
3941: malloc in them to see if additional memory allocation was needed.
3943: Developer Notes:
3944: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3945: entries or columns indices
3947: By default, this format uses inodes (identical nodes) when possible, to
3948: improve numerical efficiency of matrix-vector products and solves. We
3949: search for consecutive rows with the same nonzero structure, thereby
3950: reusing matrix information to achieve increased efficiency.
3952: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3953: `MatSeqAIJSetTotalPreallocation()`
3954: @*/
3955: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3956: {
3957: PetscFunctionBegin;
3960: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3961: PetscFunctionReturn(PETSC_SUCCESS);
3962: }
3964: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3965: {
3966: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3967: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3968: PetscInt i;
3970: PetscFunctionBegin;
3971: if (B->hash_active) {
3972: B->ops[0] = b->cops;
3973: PetscCall(PetscHMapIJVDestroy(&b->ht));
3974: PetscCall(PetscFree(b->dnz));
3975: B->hash_active = PETSC_FALSE;
3976: }
3977: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3978: if (nz == MAT_SKIP_ALLOCATION) {
3979: skipallocation = PETSC_TRUE;
3980: nz = 0;
3981: }
3982: PetscCall(PetscLayoutSetUp(B->rmap));
3983: PetscCall(PetscLayoutSetUp(B->cmap));
3985: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3986: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3987: if (nnz) {
3988: for (i = 0; i < B->rmap->n; i++) {
3989: 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]);
3990: 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);
3991: }
3992: }
3994: B->preallocated = PETSC_TRUE;
3995: if (!skipallocation) {
3996: if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
3997: if (!b->ilen) {
3998: /* b->ilen will count nonzeros in each row so far. */
3999: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
4000: } else {
4001: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
4002: }
4003: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
4004: if (!nnz) {
4005: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4006: else if (nz < 0) nz = 1;
4007: nz = PetscMin(nz, B->cmap->n);
4008: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
4009: PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
4010: } else {
4011: PetscInt64 nz64 = 0;
4012: for (i = 0; i < B->rmap->n; i++) {
4013: b->imax[i] = nnz[i];
4014: nz64 += nnz[i];
4015: }
4016: PetscCall(PetscIntCast(nz64, &nz));
4017: }
4019: /* allocate the matrix space */
4020: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4021: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
4022: PetscCall(PetscShmgetAllocateArray(B->rmap->n + 1, sizeof(PetscInt), (void **)&b->i));
4023: b->free_ij = PETSC_TRUE;
4024: if (B->structure_only) {
4025: b->free_a = PETSC_FALSE;
4026: } else {
4027: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)&b->a));
4028: b->free_a = PETSC_TRUE;
4029: }
4030: b->i[0] = 0;
4031: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4032: } else {
4033: b->free_a = PETSC_FALSE;
4034: b->free_ij = PETSC_FALSE;
4035: }
4037: if (b->ipre && nnz != b->ipre && b->imax) {
4038: /* reserve user-requested sparsity */
4039: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4040: }
4042: b->nz = 0;
4043: b->maxnz = nz;
4044: B->info.nz_unneeded = (double)b->maxnz;
4045: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4046: B->was_assembled = PETSC_FALSE;
4047: B->assembled = PETSC_FALSE;
4048: /* We simply deem preallocation has changed nonzero state. Updating the state
4049: will give clients (like AIJKokkos) a chance to know something has happened.
4050: */
4051: B->nonzerostate++;
4052: PetscFunctionReturn(PETSC_SUCCESS);
4053: }
4055: PetscErrorCode MatResetPreallocation_SeqAIJ_Private(Mat A, PetscBool *memoryreset)
4056: {
4057: Mat_SeqAIJ *a;
4058: PetscInt i;
4059: PetscBool skipreset;
4061: PetscFunctionBegin;
4064: 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()");
4065: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
4067: /* Check local size. If zero, then return */
4068: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
4070: a = (Mat_SeqAIJ *)A->data;
4071: /* if no saved info, we error out */
4072: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
4074: PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and cannot reset preallocation ");
4076: PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4077: if (skipreset) PetscCall(MatZeroEntries(A));
4078: else {
4079: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4080: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4081: a->i[0] = 0;
4082: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4083: A->preallocated = PETSC_TRUE;
4084: a->nz = 0;
4085: a->maxnz = a->i[A->rmap->n];
4086: A->info.nz_unneeded = (double)a->maxnz;
4087: A->was_assembled = PETSC_FALSE;
4088: A->assembled = PETSC_FALSE;
4089: A->nonzerostate++;
4090: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
4091: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4092: }
4093: if (memoryreset) *memoryreset = (PetscBool)!skipreset;
4094: PetscFunctionReturn(PETSC_SUCCESS);
4095: }
4097: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4098: {
4099: PetscFunctionBegin;
4100: PetscCall(MatResetPreallocation_SeqAIJ_Private(A, NULL));
4101: PetscFunctionReturn(PETSC_SUCCESS);
4102: }
4104: /*@
4105: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4107: Input Parameters:
4108: + B - the matrix
4109: . i - the indices into `j` for the start of each row (indices start with zero)
4110: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4111: - v - optional values in the matrix, use `NULL` if not provided
4113: Level: developer
4115: Notes:
4116: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4118: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4119: structure will be the union of all the previous nonzero structures.
4121: Developer Notes:
4122: 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
4123: then just copies the `v` values directly with `PetscMemcpy()`.
4125: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4127: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4128: @*/
4129: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4130: {
4131: PetscFunctionBegin;
4134: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4135: PetscFunctionReturn(PETSC_SUCCESS);
4136: }
4138: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4139: {
4140: PetscInt i;
4141: PetscInt m, n;
4142: PetscInt nz;
4143: PetscInt *nnz;
4145: PetscFunctionBegin;
4146: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4148: PetscCall(PetscLayoutSetUp(B->rmap));
4149: PetscCall(PetscLayoutSetUp(B->cmap));
4151: PetscCall(MatGetSize(B, &m, &n));
4152: PetscCall(PetscMalloc1(m + 1, &nnz));
4153: for (i = 0; i < m; i++) {
4154: nz = Ii[i + 1] - Ii[i];
4155: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4156: nnz[i] = nz;
4157: }
4158: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4159: PetscCall(PetscFree(nnz));
4161: 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));
4163: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4164: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4166: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4167: PetscFunctionReturn(PETSC_SUCCESS);
4168: }
4170: /*@
4171: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4173: Input Parameters:
4174: + A - left-hand side matrix
4175: . B - right-hand side matrix
4176: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4178: Output Parameter:
4179: . C - Kronecker product of `A` and `B`
4181: Level: intermediate
4183: Note:
4184: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4186: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4187: @*/
4188: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4189: {
4190: PetscFunctionBegin;
4195: PetscAssertPointer(C, 4);
4196: if (reuse == MAT_REUSE_MATRIX) {
4199: }
4200: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4201: PetscFunctionReturn(PETSC_SUCCESS);
4202: }
4204: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4205: {
4206: Mat newmat;
4207: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4208: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4209: PetscScalar *v;
4210: const PetscScalar *aa, *ba;
4211: 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;
4212: PetscBool flg;
4214: PetscFunctionBegin;
4215: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4216: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4217: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4218: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4219: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4220: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4221: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4222: if (reuse == MAT_INITIAL_MATRIX) {
4223: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4224: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4225: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4226: PetscCall(MatSetType(newmat, MATAIJ));
4227: i[0] = 0;
4228: for (m = 0; m < am; ++m) {
4229: for (p = 0; p < bm; ++p) {
4230: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4231: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4232: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4233: }
4234: }
4235: }
4236: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4237: *C = newmat;
4238: PetscCall(PetscFree2(i, j));
4239: nnz = 0;
4240: }
4241: PetscCall(MatSeqAIJGetArray(*C, &v));
4242: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4243: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4244: for (m = 0; m < am; ++m) {
4245: for (p = 0; p < bm; ++p) {
4246: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4247: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4248: }
4249: }
4250: }
4251: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4252: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4253: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4254: PetscFunctionReturn(PETSC_SUCCESS);
4255: }
4257: #include <../src/mat/impls/dense/seq/dense.h>
4258: #include <petsc/private/kernels/petscaxpy.h>
4260: /*
4261: Computes (B'*A')' since computing B*A directly is untenable
4263: n p p
4264: [ ] [ ] [ ]
4265: m [ A ] * n [ B ] = m [ C ]
4266: [ ] [ ] [ ]
4268: */
4269: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4270: {
4271: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4272: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4273: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4274: PetscInt i, j, n, m, q, p;
4275: const PetscInt *ii, *idx;
4276: const PetscScalar *b, *a, *a_q;
4277: PetscScalar *c, *c_q;
4278: PetscInt clda = sub_c->lda;
4279: PetscInt alda = sub_a->lda;
4281: PetscFunctionBegin;
4282: m = A->rmap->n;
4283: n = A->cmap->n;
4284: p = B->cmap->n;
4285: a = sub_a->v;
4286: b = sub_b->a;
4287: c = sub_c->v;
4288: if (clda == m) {
4289: PetscCall(PetscArrayzero(c, m * p));
4290: } else {
4291: for (j = 0; j < p; j++)
4292: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4293: }
4294: ii = sub_b->i;
4295: idx = sub_b->j;
4296: for (i = 0; i < n; i++) {
4297: q = ii[i + 1] - ii[i];
4298: while (q-- > 0) {
4299: c_q = c + clda * (*idx);
4300: a_q = a + alda * i;
4301: PetscKernelAXPY(c_q, *b, a_q, m);
4302: idx++;
4303: b++;
4304: }
4305: }
4306: PetscFunctionReturn(PETSC_SUCCESS);
4307: }
4309: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4310: {
4311: PetscInt m = A->rmap->n, n = B->cmap->n;
4312: PetscBool cisdense;
4314: PetscFunctionBegin;
4315: 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);
4316: PetscCall(MatSetSizes(C, m, n, m, n));
4317: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4318: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4319: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4320: PetscCall(MatSetUp(C));
4322: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4323: PetscFunctionReturn(PETSC_SUCCESS);
4324: }
4326: /*MC
4327: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4328: based on compressed sparse row format.
4330: Options Database Key:
4331: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4333: Level: beginner
4335: Notes:
4336: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4337: in this case the values associated with the rows and columns one passes in are set to zero
4338: in the matrix
4340: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4341: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4343: Developer Note:
4344: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4346: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4347: M*/
4349: /*MC
4350: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4352: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4353: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4354: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4355: for communicators controlling multiple processes. It is recommended that you call both of
4356: the above preallocation routines for simplicity.
4358: Options Database Key:
4359: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4361: Level: beginner
4363: Note:
4364: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4365: enough exist.
4367: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4368: M*/
4370: /*MC
4371: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4373: Options Database Key:
4374: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4376: Level: beginner
4378: Note:
4379: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4380: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4381: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4382: for communicators controlling multiple processes. It is recommended that you call both of
4383: the above preallocation routines for simplicity.
4385: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4386: M*/
4388: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4389: #if defined(PETSC_HAVE_ELEMENTAL)
4390: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4391: #endif
4392: #if defined(PETSC_HAVE_SCALAPACK)
4393: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4394: #endif
4395: #if defined(PETSC_HAVE_HYPRE)
4396: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4397: #endif
4399: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4400: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4401: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4403: /*@C
4404: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4406: Not Collective
4408: Input Parameter:
4409: . A - a `MATSEQAIJ` matrix
4411: Output Parameter:
4412: . array - pointer to the data
4414: Level: intermediate
4416: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`
4417: @*/
4418: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar *array[])
4419: {
4420: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4422: PetscFunctionBegin;
4423: if (aij->ops->getarray) {
4424: PetscCall((*aij->ops->getarray)(A, array));
4425: } else {
4426: *array = aij->a;
4427: }
4428: PetscFunctionReturn(PETSC_SUCCESS);
4429: }
4431: /*@C
4432: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4434: Not Collective
4436: Input Parameters:
4437: + A - a `MATSEQAIJ` matrix
4438: - array - pointer to the data
4440: Level: intermediate
4442: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`
4443: @*/
4444: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar *array[])
4445: {
4446: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4448: PetscFunctionBegin;
4449: if (aij->ops->restorearray) {
4450: PetscCall((*aij->ops->restorearray)(A, array));
4451: } else {
4452: *array = NULL;
4453: }
4454: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4455: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4456: PetscFunctionReturn(PETSC_SUCCESS);
4457: }
4459: /*@C
4460: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4462: Not Collective; No Fortran Support
4464: Input Parameter:
4465: . A - a `MATSEQAIJ` matrix
4467: Output Parameter:
4468: . array - pointer to the data
4470: Level: intermediate
4472: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4473: @*/
4474: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar *array[])
4475: {
4476: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4478: PetscFunctionBegin;
4479: if (aij->ops->getarrayread) {
4480: PetscCall((*aij->ops->getarrayread)(A, array));
4481: } else {
4482: *array = aij->a;
4483: }
4484: PetscFunctionReturn(PETSC_SUCCESS);
4485: }
4487: /*@C
4488: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4490: Not Collective; No Fortran Support
4492: Input Parameter:
4493: . A - a `MATSEQAIJ` matrix
4495: Output Parameter:
4496: . array - pointer to the data
4498: Level: intermediate
4500: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4501: @*/
4502: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar *array[])
4503: {
4504: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4506: PetscFunctionBegin;
4507: if (aij->ops->restorearrayread) {
4508: PetscCall((*aij->ops->restorearrayread)(A, array));
4509: } else {
4510: *array = NULL;
4511: }
4512: PetscFunctionReturn(PETSC_SUCCESS);
4513: }
4515: /*@C
4516: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4518: Not Collective; No Fortran Support
4520: Input Parameter:
4521: . A - a `MATSEQAIJ` matrix
4523: Output Parameter:
4524: . array - pointer to the data
4526: Level: intermediate
4528: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4529: @*/
4530: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar *array[])
4531: {
4532: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4534: PetscFunctionBegin;
4535: if (aij->ops->getarraywrite) {
4536: PetscCall((*aij->ops->getarraywrite)(A, array));
4537: } else {
4538: *array = aij->a;
4539: }
4540: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4541: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4542: PetscFunctionReturn(PETSC_SUCCESS);
4543: }
4545: /*@C
4546: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4548: Not Collective; No Fortran Support
4550: Input Parameter:
4551: . A - a MATSEQAIJ matrix
4553: Output Parameter:
4554: . array - pointer to the data
4556: Level: intermediate
4558: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4559: @*/
4560: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar *array[])
4561: {
4562: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4564: PetscFunctionBegin;
4565: if (aij->ops->restorearraywrite) {
4566: PetscCall((*aij->ops->restorearraywrite)(A, array));
4567: } else {
4568: *array = NULL;
4569: }
4570: PetscFunctionReturn(PETSC_SUCCESS);
4571: }
4573: /*@C
4574: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4576: Not Collective; No Fortran Support
4578: Input Parameter:
4579: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4581: Output Parameters:
4582: + i - row map array of the matrix
4583: . j - column index array of the matrix
4584: . a - data array of the matrix
4585: - mtype - memory type of the arrays
4587: Level: developer
4589: Notes:
4590: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4591: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4593: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4594: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4596: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4597: @*/
4598: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt *i[], const PetscInt *j[], PetscScalar *a[], PetscMemType *mtype)
4599: {
4600: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4602: PetscFunctionBegin;
4603: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4604: if (aij->ops->getcsrandmemtype) {
4605: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4606: } else {
4607: if (i) *i = aij->i;
4608: if (j) *j = aij->j;
4609: if (a) *a = aij->a;
4610: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4611: }
4612: PetscFunctionReturn(PETSC_SUCCESS);
4613: }
4615: /*@
4616: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4618: Not Collective
4620: Input Parameter:
4621: . A - a `MATSEQAIJ` matrix
4623: Output Parameter:
4624: . nz - the maximum number of nonzeros in any row
4626: Level: intermediate
4628: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`
4629: @*/
4630: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4631: {
4632: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4634: PetscFunctionBegin;
4635: *nz = aij->rmax;
4636: PetscFunctionReturn(PETSC_SUCCESS);
4637: }
4639: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void **data)
4640: {
4641: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)*data;
4643: PetscFunctionBegin;
4644: PetscCall(PetscFree(coo->perm));
4645: PetscCall(PetscFree(coo->jmap));
4646: PetscCall(PetscFree(coo));
4647: PetscFunctionReturn(PETSC_SUCCESS);
4648: }
4650: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4651: {
4652: MPI_Comm comm;
4653: PetscInt *i, *j;
4654: PetscInt M, N, row, iprev;
4655: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4656: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4657: PetscInt *Aj;
4658: PetscScalar *Aa;
4659: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)mat->data;
4660: MatType rtype;
4661: PetscCount *perm, *jmap;
4662: MatCOOStruct_SeqAIJ *coo;
4663: PetscBool isorted;
4664: PetscBool hypre;
4665: const char *name;
4667: PetscFunctionBegin;
4668: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4669: PetscCall(MatGetSize(mat, &M, &N));
4670: i = coo_i;
4671: j = coo_j;
4672: PetscCall(PetscMalloc1(coo_n, &perm));
4674: /* 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) */
4675: isorted = PETSC_TRUE;
4676: iprev = PETSC_INT_MIN;
4677: for (k = 0; k < coo_n; k++) {
4678: if (j[k] < 0) i[k] = -1;
4679: if (isorted) {
4680: if (i[k] < iprev) isorted = PETSC_FALSE;
4681: else iprev = i[k];
4682: }
4683: perm[k] = k;
4684: }
4686: /* Sort by row if not already */
4687: if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4689: /* Advance k to the first row with a non-negative index */
4690: for (k = 0; k < coo_n; k++)
4691: if (i[k] >= 0) break;
4692: nneg = k;
4693: 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 */
4694: nnz = 0; /* Total number of unique nonzeros to be counted */
4695: jmap++; /* Inc jmap by 1 for convenience */
4697: PetscCall(PetscShmgetAllocateArray(M + 1, sizeof(PetscInt), (void **)&Ai)); /* CSR of A */
4698: PetscCall(PetscArrayzero(Ai, M + 1));
4699: PetscCall(PetscShmgetAllocateArray(coo_n - nneg, sizeof(PetscInt), (void **)&Aj)); /* We have at most coo_n-nneg unique nonzeros */
4701: PetscCall(PetscObjectGetName((PetscObject)mat, &name));
4702: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
4704: /* In each row, sort by column, then unique column indices to get row length */
4705: Ai++; /* Inc by 1 for convenience */
4706: q = 0; /* q-th unique nonzero, with q starting from 0 */
4707: while (k < coo_n) {
4708: PetscBool strictly_sorted; // this row is strictly sorted?
4709: PetscInt jprev;
4711: /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4712: row = i[k];
4713: start = k;
4714: jprev = PETSC_INT_MIN;
4715: strictly_sorted = PETSC_TRUE;
4716: while (k < coo_n && i[k] == row) {
4717: if (strictly_sorted) {
4718: if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4719: else jprev = j[k];
4720: }
4721: k++;
4722: }
4723: end = k;
4725: /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4726: if (hypre) {
4727: PetscInt minj = PETSC_INT_MAX;
4728: PetscBool hasdiag = PETSC_FALSE;
4730: if (strictly_sorted) { // fast path to swap the first and the diag
4731: PetscCount tmp;
4732: for (p = start; p < end; p++) {
4733: if (j[p] == row && p != start) {
4734: j[p] = j[start]; // swap j[], so that the diagonal value will go first (manipulated by perm[])
4735: j[start] = row;
4736: tmp = perm[start];
4737: perm[start] = perm[p]; // also swap perm[] so we can save the call to PetscSortIntWithCountArray() below
4738: perm[p] = tmp;
4739: break;
4740: }
4741: }
4742: } else {
4743: for (p = start; p < end; p++) {
4744: hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4745: minj = PetscMin(minj, j[p]);
4746: }
4748: if (hasdiag) {
4749: for (p = start; p < end; p++) {
4750: if (j[p] == minj) j[p] = row;
4751: else if (j[p] == row) j[p] = minj;
4752: }
4753: }
4754: }
4755: }
4756: // sort by columns in a row. perm[] indicates their original order
4757: if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4759: if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4760: for (p = start; p < end; p++, q++) {
4761: Aj[q] = j[p];
4762: jmap[q] = 1;
4763: }
4764: PetscCall(PetscIntCast(end - start, Ai + row));
4765: nnz += Ai[row]; // q is already advanced
4766: } else {
4767: /* Find number of unique col entries in this row */
4768: Aj[q] = j[start]; /* Log the first nonzero in this row */
4769: jmap[q] = 1; /* Number of repeats of this nonzero entry */
4770: Ai[row] = 1;
4771: nnz++;
4773: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4774: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4775: q++;
4776: jmap[q] = 1;
4777: Aj[q] = j[p];
4778: Ai[row]++;
4779: nnz++;
4780: } else {
4781: jmap[q]++;
4782: }
4783: }
4784: q++; /* Move to next row and thus next unique nonzero */
4785: }
4786: }
4788: Ai--; /* Back to the beginning of Ai[] */
4789: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4790: jmap--; // Back to the beginning of jmap[]
4791: jmap[0] = 0;
4792: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4794: if (nnz < coo_n - nneg) { /* Reallocate with actual number of unique nonzeros */
4795: PetscCount *jmap_new;
4796: PetscInt *Aj_new;
4798: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4799: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4800: PetscCall(PetscFree(jmap));
4801: jmap = jmap_new;
4803: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscInt), (void **)&Aj_new));
4804: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4805: PetscCall(PetscShmgetDeallocateArray((void **)&Aj));
4806: Aj = Aj_new;
4807: }
4809: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4810: PetscCount *perm_new;
4812: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4813: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4814: PetscCall(PetscFree(perm));
4815: perm = perm_new;
4816: }
4818: PetscCall(MatGetRootType_Private(mat, &rtype));
4819: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscScalar), (void **)&Aa));
4820: PetscCall(PetscArrayzero(Aa, nnz));
4821: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4823: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4825: // Put the COO struct in a container and then attach that to the matrix
4826: PetscCall(PetscMalloc1(1, &coo));
4827: PetscCall(PetscIntCast(nnz, &coo->nz));
4828: coo->n = coo_n;
4829: coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4830: coo->jmap = jmap; // of length nnz+1
4831: coo->perm = perm;
4832: PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", coo, MatCOOStructDestroy_SeqAIJ));
4833: PetscFunctionReturn(PETSC_SUCCESS);
4834: }
4836: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4837: {
4838: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4839: PetscCount i, j, Annz = aseq->nz;
4840: PetscCount *perm, *jmap;
4841: PetscScalar *Aa;
4842: PetscContainer container;
4843: MatCOOStruct_SeqAIJ *coo;
4845: PetscFunctionBegin;
4846: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4847: PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4848: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4849: perm = coo->perm;
4850: jmap = coo->jmap;
4851: PetscCall(MatSeqAIJGetArray(A, &Aa));
4852: for (i = 0; i < Annz; i++) {
4853: PetscScalar sum = 0.0;
4854: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4855: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4856: }
4857: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4858: PetscFunctionReturn(PETSC_SUCCESS);
4859: }
4861: #if defined(PETSC_HAVE_CUDA)
4862: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4863: #endif
4864: #if defined(PETSC_HAVE_HIP)
4865: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4866: #endif
4867: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4868: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4869: #endif
4871: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4872: {
4873: Mat_SeqAIJ *b;
4874: PetscMPIInt size;
4876: PetscFunctionBegin;
4877: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4878: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4880: PetscCall(PetscNew(&b));
4882: B->data = (void *)b;
4883: B->ops[0] = MatOps_Values;
4884: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4886: b->row = NULL;
4887: b->col = NULL;
4888: b->icol = NULL;
4889: b->reallocs = 0;
4890: b->ignorezeroentries = PETSC_FALSE;
4891: b->roworiented = PETSC_TRUE;
4892: b->nonew = 0;
4893: b->diag = NULL;
4894: b->solve_work = NULL;
4895: B->spptr = NULL;
4896: b->saved_values = NULL;
4897: b->idiag = NULL;
4898: b->mdiag = NULL;
4899: b->ssor_work = NULL;
4900: b->omega = 1.0;
4901: b->fshift = 0.0;
4902: b->idiagvalid = PETSC_FALSE;
4903: b->ibdiagvalid = PETSC_FALSE;
4904: b->keepnonzeropattern = PETSC_FALSE;
4906: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4907: #if defined(PETSC_HAVE_MATLAB)
4908: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4910: #endif
4911: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4914: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4915: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4918: #if defined(PETSC_HAVE_MKL_SPARSE)
4919: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4920: #endif
4921: #if defined(PETSC_HAVE_CUDA)
4922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4923: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4925: #endif
4926: #if defined(PETSC_HAVE_HIP)
4927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4930: #endif
4931: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4933: #endif
4934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4935: #if defined(PETSC_HAVE_ELEMENTAL)
4936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4937: #endif
4938: #if defined(PETSC_HAVE_SCALAPACK)
4939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4940: #endif
4941: #if defined(PETSC_HAVE_HYPRE)
4942: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4943: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4944: #endif
4945: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4946: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4949: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4950: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4951: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4952: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_SeqAIJ));
4953: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4954: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4955: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4956: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4957: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4958: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4959: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4960: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4961: PetscCall(MatCreate_SeqAIJ_Inode(B));
4962: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4963: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4964: PetscFunctionReturn(PETSC_SUCCESS);
4965: }
4967: /*
4968: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4969: */
4970: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4971: {
4972: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4973: PetscInt m = A->rmap->n, i;
4975: PetscFunctionBegin;
4976: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4978: C->factortype = A->factortype;
4979: c->row = NULL;
4980: c->col = NULL;
4981: c->icol = NULL;
4982: c->reallocs = 0;
4983: c->diagonaldense = a->diagonaldense;
4985: C->assembled = A->assembled;
4987: if (A->preallocated) {
4988: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4989: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4991: if (!A->hash_active) {
4992: PetscCall(PetscMalloc1(m, &c->imax));
4993: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4994: PetscCall(PetscMalloc1(m, &c->ilen));
4995: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
4997: /* allocate the matrix space */
4998: if (mallocmatspace) {
4999: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscScalar), (void **)&c->a));
5000: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscInt), (void **)&c->j));
5001: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
5002: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
5003: c->free_a = PETSC_TRUE;
5004: c->free_ij = PETSC_TRUE;
5005: if (m > 0) {
5006: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
5007: if (cpvalues == MAT_COPY_VALUES) {
5008: const PetscScalar *aa;
5010: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5011: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5012: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5013: } else {
5014: PetscCall(PetscArrayzero(c->a, a->i[m]));
5015: }
5016: }
5017: }
5018: C->preallocated = PETSC_TRUE;
5019: } else {
5020: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5021: PetscCall(MatSetUp(C));
5022: }
5024: c->ignorezeroentries = a->ignorezeroentries;
5025: c->roworiented = a->roworiented;
5026: c->nonew = a->nonew;
5027: if (a->diag) {
5028: PetscCall(PetscMalloc1(m + 1, &c->diag));
5029: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5030: } else c->diag = NULL;
5032: c->solve_work = NULL;
5033: c->saved_values = NULL;
5034: c->idiag = NULL;
5035: c->ssor_work = NULL;
5036: c->keepnonzeropattern = a->keepnonzeropattern;
5038: c->rmax = a->rmax;
5039: c->nz = a->nz;
5040: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
5042: c->compressedrow.use = a->compressedrow.use;
5043: c->compressedrow.nrows = a->compressedrow.nrows;
5044: if (a->compressedrow.use) {
5045: i = a->compressedrow.nrows;
5046: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5047: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5048: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5049: } else {
5050: c->compressedrow.use = PETSC_FALSE;
5051: c->compressedrow.i = NULL;
5052: c->compressedrow.rindex = NULL;
5053: }
5054: c->nonzerorowcnt = a->nonzerorowcnt;
5055: C->nonzerostate = A->nonzerostate;
5057: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5058: }
5059: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5060: PetscFunctionReturn(PETSC_SUCCESS);
5061: }
5063: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5064: {
5065: PetscFunctionBegin;
5066: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5067: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5068: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5069: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5070: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5071: PetscFunctionReturn(PETSC_SUCCESS);
5072: }
5074: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5075: {
5076: PetscBool isbinary, ishdf5;
5078: PetscFunctionBegin;
5081: /* force binary viewer to load .info file if it has not yet done so */
5082: PetscCall(PetscViewerSetUp(viewer));
5083: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5084: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5085: if (isbinary) {
5086: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5087: } else if (ishdf5) {
5088: #if defined(PETSC_HAVE_HDF5)
5089: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5090: #else
5091: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5092: #endif
5093: } else {
5094: 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);
5095: }
5096: PetscFunctionReturn(PETSC_SUCCESS);
5097: }
5099: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5100: {
5101: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5102: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
5104: PetscFunctionBegin;
5105: PetscCall(PetscViewerSetUp(viewer));
5107: /* read in matrix header */
5108: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5109: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5110: M = header[1];
5111: N = header[2];
5112: nz = header[3];
5113: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5114: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5115: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5117: /* set block sizes from the viewer's .info file */
5118: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5119: /* set local and global sizes if not set already */
5120: if (mat->rmap->n < 0) mat->rmap->n = M;
5121: if (mat->cmap->n < 0) mat->cmap->n = N;
5122: if (mat->rmap->N < 0) mat->rmap->N = M;
5123: if (mat->cmap->N < 0) mat->cmap->N = N;
5124: PetscCall(PetscLayoutSetUp(mat->rmap));
5125: PetscCall(PetscLayoutSetUp(mat->cmap));
5127: /* check if the matrix sizes are correct */
5128: PetscCall(MatGetSize(mat, &rows, &cols));
5129: 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);
5131: /* read in row lengths */
5132: PetscCall(PetscMalloc1(M, &rowlens));
5133: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5134: /* check if sum(rowlens) is same as nz */
5135: sum = 0;
5136: for (i = 0; i < M; i++) sum += rowlens[i];
5137: 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);
5138: /* preallocate and check sizes */
5139: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5140: PetscCall(MatGetSize(mat, &rows, &cols));
5141: 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);
5142: /* store row lengths */
5143: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5144: PetscCall(PetscFree(rowlens));
5146: /* fill in "i" row pointers */
5147: a->i[0] = 0;
5148: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5149: /* read in "j" column indices */
5150: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5151: /* read in "a" nonzero values */
5152: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5154: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5155: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5156: PetscFunctionReturn(PETSC_SUCCESS);
5157: }
5159: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5160: {
5161: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5162: const PetscScalar *aa, *ba;
5163: #if defined(PETSC_USE_COMPLEX)
5164: PetscInt k;
5165: #endif
5167: PetscFunctionBegin;
5168: /* If the matrix dimensions are not equal,or no of nonzeros */
5169: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5170: *flg = PETSC_FALSE;
5171: PetscFunctionReturn(PETSC_SUCCESS);
5172: }
5174: /* if the a->i are the same */
5175: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5176: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5178: /* if a->j are the same */
5179: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5180: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5182: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5183: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5184: /* if a->a are the same */
5185: #if defined(PETSC_USE_COMPLEX)
5186: for (k = 0; k < a->nz; k++) {
5187: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5188: *flg = PETSC_FALSE;
5189: PetscFunctionReturn(PETSC_SUCCESS);
5190: }
5191: }
5192: #else
5193: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5194: #endif
5195: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5196: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5197: PetscFunctionReturn(PETSC_SUCCESS);
5198: }
5200: /*@
5201: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5202: provided by the user.
5204: Collective
5206: Input Parameters:
5207: + comm - must be an MPI communicator of size 1
5208: . m - number of rows
5209: . n - number of columns
5210: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5211: . j - column indices
5212: - a - matrix values
5214: Output Parameter:
5215: . mat - the matrix
5217: Level: intermediate
5219: Notes:
5220: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5221: once the matrix is destroyed and not before
5223: You cannot set new nonzero locations into this matrix, that will generate an error.
5225: The `i` and `j` indices are 0 based
5227: The format which is used for the sparse matrix input, is equivalent to a
5228: row-major ordering.. i.e for the following matrix, the input data expected is
5229: as shown
5230: .vb
5231: 1 0 0
5232: 2 0 3
5233: 4 5 6
5235: i = {0,1,3,6} [size = nrow+1 = 3+1]
5236: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5237: v = {1,2,3,4,5,6} [size = 6]
5238: .ve
5240: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5241: @*/
5242: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5243: {
5244: PetscInt ii;
5245: Mat_SeqAIJ *aij;
5246: PetscInt jj;
5248: PetscFunctionBegin;
5249: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5250: PetscCall(MatCreate(comm, mat));
5251: PetscCall(MatSetSizes(*mat, m, n, m, n));
5252: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5253: PetscCall(MatSetType(*mat, MATSEQAIJ));
5254: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5255: aij = (Mat_SeqAIJ *)(*mat)->data;
5256: PetscCall(PetscMalloc1(m, &aij->imax));
5257: PetscCall(PetscMalloc1(m, &aij->ilen));
5259: aij->i = i;
5260: aij->j = j;
5261: aij->a = a;
5262: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5263: aij->free_a = PETSC_FALSE;
5264: aij->free_ij = PETSC_FALSE;
5266: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5267: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5268: if (PetscDefined(USE_DEBUG)) {
5269: 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]);
5270: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5271: 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);
5272: 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);
5273: }
5274: }
5275: }
5276: if (PetscDefined(USE_DEBUG)) {
5277: for (ii = 0; ii < aij->i[m]; ii++) {
5278: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5279: 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);
5280: }
5281: }
5283: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5284: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5285: PetscFunctionReturn(PETSC_SUCCESS);
5286: }
5288: /*@
5289: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5290: provided by the user.
5292: Collective
5294: Input Parameters:
5295: + comm - must be an MPI communicator of size 1
5296: . m - number of rows
5297: . n - number of columns
5298: . i - row indices
5299: . j - column indices
5300: . a - matrix values
5301: . nz - number of nonzeros
5302: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5304: Output Parameter:
5305: . mat - the matrix
5307: Level: intermediate
5309: Example:
5310: For the following matrix, the input data expected is as shown (using 0 based indexing)
5311: .vb
5312: 1 0 0
5313: 2 0 3
5314: 4 5 6
5316: i = {0,1,1,2,2,2}
5317: j = {0,0,2,0,1,2}
5318: v = {1,2,3,4,5,6}
5319: .ve
5321: Note:
5322: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5323: and are particularly useful in iterative applications.
5325: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5326: @*/
5327: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscCount nz, PetscBool idx)
5328: {
5329: PetscInt ii, *nnz, one = 1, row, col;
5331: PetscFunctionBegin;
5332: PetscCall(PetscCalloc1(m, &nnz));
5333: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5334: PetscCall(MatCreate(comm, mat));
5335: PetscCall(MatSetSizes(*mat, m, n, m, n));
5336: PetscCall(MatSetType(*mat, MATSEQAIJ));
5337: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5338: for (ii = 0; ii < nz; ii++) {
5339: if (idx) {
5340: row = i[ii] - 1;
5341: col = j[ii] - 1;
5342: } else {
5343: row = i[ii];
5344: col = j[ii];
5345: }
5346: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5347: }
5348: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5349: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5350: PetscCall(PetscFree(nnz));
5351: PetscFunctionReturn(PETSC_SUCCESS);
5352: }
5354: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5355: {
5356: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5358: PetscFunctionBegin;
5359: a->idiagvalid = PETSC_FALSE;
5360: a->ibdiagvalid = PETSC_FALSE;
5362: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5363: PetscFunctionReturn(PETSC_SUCCESS);
5364: }
5366: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5367: {
5368: PetscFunctionBegin;
5369: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5370: PetscFunctionReturn(PETSC_SUCCESS);
5371: }
5373: /*
5374: Permute A into C's *local* index space using rowemb,colemb.
5375: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5376: of [0,m), colemb is in [0,n).
5377: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5378: */
5379: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5380: {
5381: /* If making this function public, change the error returned in this function away from _PLIB. */
5382: Mat_SeqAIJ *Baij;
5383: PetscBool seqaij;
5384: PetscInt m, n, *nz, i, j, count;
5385: PetscScalar v;
5386: const PetscInt *rowindices, *colindices;
5388: PetscFunctionBegin;
5389: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5390: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5391: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5392: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5393: if (rowemb) {
5394: PetscCall(ISGetLocalSize(rowemb, &m));
5395: 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);
5396: } else {
5397: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5398: }
5399: if (colemb) {
5400: PetscCall(ISGetLocalSize(colemb, &n));
5401: 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);
5402: } else {
5403: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5404: }
5406: Baij = (Mat_SeqAIJ *)B->data;
5407: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5408: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5409: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5410: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5411: PetscCall(PetscFree(nz));
5412: }
5413: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5414: count = 0;
5415: rowindices = NULL;
5416: colindices = NULL;
5417: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5418: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5419: for (i = 0; i < B->rmap->n; i++) {
5420: PetscInt row;
5421: row = i;
5422: if (rowindices) row = rowindices[i];
5423: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5424: PetscInt col;
5425: col = Baij->j[count];
5426: if (colindices) col = colindices[col];
5427: v = Baij->a[count];
5428: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5429: ++count;
5430: }
5431: }
5432: /* FIXME: set C's nonzerostate correctly. */
5433: /* Assembly for C is necessary. */
5434: C->preallocated = PETSC_TRUE;
5435: C->assembled = PETSC_TRUE;
5436: C->was_assembled = PETSC_FALSE;
5437: PetscFunctionReturn(PETSC_SUCCESS);
5438: }
5440: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5441: {
5442: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5443: MatScalar *aa = a->a;
5444: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5445: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5447: PetscFunctionBegin;
5448: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5449: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5450: for (i = 1; i <= m; i++) {
5451: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5452: for (k = ai[i - 1]; k < ai[i]; k++) {
5453: if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5454: else {
5455: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5456: aa[k - fshift] = aa[k];
5457: aj[k - fshift] = aj[k];
5458: }
5459: }
5460: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5461: fshift_prev = fshift;
5462: /* reset ilen and imax for each row */
5463: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5464: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5465: rmax = PetscMax(rmax, ailen[i - 1]);
5466: }
5467: if (fshift) {
5468: if (m) {
5469: ai[m] -= fshift;
5470: a->nz = ai[m];
5471: }
5472: 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));
5473: A->nonzerostate++;
5474: A->info.nz_unneeded += (PetscReal)fshift;
5475: a->rmax = rmax;
5476: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5477: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5478: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5479: }
5480: PetscFunctionReturn(PETSC_SUCCESS);
5481: }
5483: PetscFunctionList MatSeqAIJList = NULL;
5485: /*@
5486: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5488: Collective
5490: Input Parameters:
5491: + mat - the matrix object
5492: - matype - matrix type
5494: Options Database Key:
5495: . -mat_seqaij_type <method> - for example seqaijcrl
5497: Level: intermediate
5499: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5500: @*/
5501: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5502: {
5503: PetscBool sametype;
5504: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5506: PetscFunctionBegin;
5508: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5509: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5511: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5512: PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5513: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5514: PetscFunctionReturn(PETSC_SUCCESS);
5515: }
5517: /*@C
5518: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5520: Not Collective, No Fortran Support
5522: Input Parameters:
5523: + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5524: - function - routine to convert to subtype
5526: Level: advanced
5528: Notes:
5529: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5531: Then, your matrix can be chosen with the procedural interface at runtime via the option
5532: $ -mat_seqaij_type my_mat
5534: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5535: @*/
5536: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5537: {
5538: PetscFunctionBegin;
5539: PetscCall(MatInitializePackage());
5540: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5541: PetscFunctionReturn(PETSC_SUCCESS);
5542: }
5544: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5546: /*@C
5547: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5549: Not Collective
5551: Level: advanced
5553: Note:
5554: This registers the versions of `MATSEQAIJ` for GPUs
5556: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5557: @*/
5558: PetscErrorCode MatSeqAIJRegisterAll(void)
5559: {
5560: PetscFunctionBegin;
5561: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5562: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5564: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5565: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5566: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5567: #if defined(PETSC_HAVE_MKL_SPARSE)
5568: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5569: #endif
5570: #if defined(PETSC_HAVE_CUDA)
5571: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5572: #endif
5573: #if defined(PETSC_HAVE_HIP)
5574: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5575: #endif
5576: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5577: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5578: #endif
5579: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5580: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5581: #endif
5582: PetscFunctionReturn(PETSC_SUCCESS);
5583: }
5585: /*
5586: Special version for direct calls from Fortran
5587: */
5588: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5589: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5590: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5591: #define matsetvaluesseqaij_ matsetvaluesseqaij
5592: #endif
5594: /* Change these macros so can be used in void function */
5596: /* Change these macros so can be used in void function */
5597: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5598: #undef PetscCall
5599: #define PetscCall(...) \
5600: do { \
5601: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5602: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5603: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5604: return; \
5605: } \
5606: } while (0)
5608: #undef SETERRQ
5609: #define SETERRQ(comm, ierr, ...) \
5610: do { \
5611: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5612: return; \
5613: } while (0)
5615: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5616: {
5617: Mat A = *AA;
5618: PetscInt m = *mm, n = *nn;
5619: InsertMode is = *isis;
5620: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5621: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5622: PetscInt *imax, *ai, *ailen;
5623: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5624: MatScalar *ap, value, *aa;
5625: PetscBool ignorezeroentries = a->ignorezeroentries;
5626: PetscBool roworiented = a->roworiented;
5628: PetscFunctionBegin;
5629: MatCheckPreallocated(A, 1);
5630: imax = a->imax;
5631: ai = a->i;
5632: ailen = a->ilen;
5633: aj = a->j;
5634: aa = a->a;
5636: for (k = 0; k < m; k++) { /* loop over added rows */
5637: row = im[k];
5638: if (row < 0) continue;
5639: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5640: rp = aj + ai[row];
5641: ap = aa + ai[row];
5642: rmax = imax[row];
5643: nrow = ailen[row];
5644: low = 0;
5645: high = nrow;
5646: for (l = 0; l < n; l++) { /* loop over added columns */
5647: if (in[l] < 0) continue;
5648: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5649: col = in[l];
5650: if (roworiented) value = v[l + k * n];
5651: else value = v[k + l * m];
5653: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5655: if (col <= lastcol) low = 0;
5656: else high = nrow;
5657: lastcol = col;
5658: while (high - low > 5) {
5659: t = (low + high) / 2;
5660: if (rp[t] > col) high = t;
5661: else low = t;
5662: }
5663: for (i = low; i < high; i++) {
5664: if (rp[i] > col) break;
5665: if (rp[i] == col) {
5666: if (is == ADD_VALUES) ap[i] += value;
5667: else ap[i] = value;
5668: goto noinsert;
5669: }
5670: }
5671: if (value == 0.0 && ignorezeroentries) goto noinsert;
5672: if (nonew == 1) goto noinsert;
5673: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5674: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5675: N = nrow++ - 1;
5676: a->nz++;
5677: high++;
5678: /* shift up all the later entries in this row */
5679: for (ii = N; ii >= i; ii--) {
5680: rp[ii + 1] = rp[ii];
5681: ap[ii + 1] = ap[ii];
5682: }
5683: rp[i] = col;
5684: ap[i] = value;
5685: noinsert:;
5686: low = i + 1;
5687: }
5688: ailen[row] = nrow;
5689: }
5690: PetscFunctionReturnVoid();
5691: }
5692: /* Undefining these here since they were redefined from their original definition above! No
5693: * other PETSc functions should be defined past this point, as it is impossible to recover the
5694: * original definitions */
5695: #undef PetscCall
5696: #undef SETERRQ