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: MatGetDiagonalMarkers(SeqAIJ, 1)
21: static PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
22: {
23: PetscBool flg;
24: char type[256];
26: PetscFunctionBegin;
27: PetscObjectOptionsBegin((PetscObject)A);
28: PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg));
29: if (flg) PetscCall(MatSeqAIJSetType(A, type));
30: PetscOptionsEnd();
31: PetscFunctionReturn(PETSC_SUCCESS);
32: }
34: static PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions)
35: {
36: PetscInt i, m, n;
37: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
39: PetscFunctionBegin;
40: PetscCall(MatGetSize(A, &m, &n));
41: PetscCall(PetscArrayzero(reductions, n));
42: if (type == NORM_2) {
43: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]);
44: } else if (type == NORM_1) {
45: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
46: } else if (type == NORM_INFINITY) {
47: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]);
48: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
49: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
50: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
51: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
52: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
54: if (type == NORM_2) {
55: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
56: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
57: for (i = 0; i < n; i++) reductions[i] /= m;
58: }
59: PetscFunctionReturn(PETSC_SUCCESS);
60: }
62: static PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is)
63: {
64: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
65: PetscInt i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs;
66: const PetscInt *jj = a->j, *ii = a->i;
67: PetscInt *rows;
69: PetscFunctionBegin;
70: for (i = 0; i < m; i++) {
71: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++;
72: }
73: PetscCall(PetscMalloc1(cnt, &rows));
74: cnt = 0;
75: for (i = 0; i < m; i++) {
76: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) {
77: rows[cnt] = i;
78: cnt++;
79: }
80: }
81: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is));
82: PetscFunctionReturn(PETSC_SUCCESS);
83: }
85: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows)
86: {
87: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
88: const MatScalar *aa;
89: PetscInt i, m = A->rmap->n, cnt = 0;
90: const PetscInt *ii = a->i, *jj = a->j, *diag;
91: PetscInt *rows;
93: PetscFunctionBegin;
94: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
95: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, NULL));
96: for (i = 0; i < m; i++) {
97: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++;
98: }
99: PetscCall(PetscMalloc1(cnt, &rows));
100: cnt = 0;
101: for (i = 0; i < m; i++) {
102: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i;
103: }
104: *nrows = cnt;
105: *zrows = rows;
106: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
107: PetscFunctionReturn(PETSC_SUCCESS);
108: }
110: static PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows)
111: {
112: PetscInt nrows, *rows;
114: PetscFunctionBegin;
115: *zrows = NULL;
116: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows));
117: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows));
118: PetscFunctionReturn(PETSC_SUCCESS);
119: }
121: static PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows)
122: {
123: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
124: const MatScalar *aa;
125: PetscInt m = A->rmap->n, cnt = 0;
126: const PetscInt *ii;
127: PetscInt n, i, j, *rows;
129: PetscFunctionBegin;
130: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
131: *keptrows = NULL;
132: ii = a->i;
133: for (i = 0; i < m; i++) {
134: n = ii[i + 1] - ii[i];
135: if (!n) {
136: cnt++;
137: goto ok1;
138: }
139: for (j = ii[i]; j < ii[i + 1]; j++) {
140: if (aa[j] != 0.0) goto ok1;
141: }
142: cnt++;
143: ok1:;
144: }
145: if (!cnt) {
146: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
147: PetscFunctionReturn(PETSC_SUCCESS);
148: }
149: PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows));
150: cnt = 0;
151: for (i = 0; i < m; i++) {
152: n = ii[i + 1] - ii[i];
153: if (!n) continue;
154: for (j = ii[i]; j < ii[i + 1]; j++) {
155: if (aa[j] != 0.0) {
156: rows[cnt++] = i;
157: break;
158: }
159: }
160: }
161: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
162: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows));
163: PetscFunctionReturn(PETSC_SUCCESS);
164: }
166: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is)
167: {
168: PetscInt i, m = Y->rmap->n;
169: const PetscInt *diag;
170: MatScalar *aa;
171: const PetscScalar *v;
172: PetscBool diagDense;
174: PetscFunctionBegin;
175: if (Y->assembled) {
176: PetscCall(MatGetDiagonalMarkers_SeqAIJ(Y, &diag, &diagDense));
177: if (diagDense) {
178: PetscCall(VecGetArrayRead(D, &v));
179: PetscCall(MatSeqAIJGetArray(Y, &aa));
180: if (is == INSERT_VALUES) {
181: for (i = 0; i < m; i++) aa[diag[i]] = v[i];
182: } else {
183: for (i = 0; i < m; i++) aa[diag[i]] += v[i];
184: }
185: PetscCall(MatSeqAIJRestoreArray(Y, &aa));
186: PetscCall(VecRestoreArrayRead(D, &v));
187: PetscFunctionReturn(PETSC_SUCCESS);
188: }
189: }
190: PetscCall(MatDiagonalSet_Default(Y, D, is));
191: PetscFunctionReturn(PETSC_SUCCESS);
192: }
194: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
195: {
196: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
197: PetscInt i, ishift;
199: PetscFunctionBegin;
200: if (m) *m = A->rmap->n;
201: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
202: ishift = 0;
203: if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) {
204: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja));
205: } else if (oshift == 1) {
206: PetscInt *tia;
207: PetscInt nz = a->i[A->rmap->n];
209: /* malloc space and add 1 to i and j indices */
210: PetscCall(PetscMalloc1(A->rmap->n + 1, &tia));
211: for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1;
212: *ia = tia;
213: if (ja) {
214: 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(PetscArraycpy(rp, in, n));
498: if (!A->structure_only) {
499: if (v) {
500: PetscCall(PetscArraycpy(ap, v, n));
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(PetscArraycpy(rp, in, n));
578: if (!A->structure_only) {
579: if (v) {
580: PetscCall(PetscArraycpy(ap, v, n));
581: v += n;
582: } else {
583: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
584: }
585: }
586: ailen[row] = n;
587: a->nz += n;
588: }
589: PetscCall(MatSeqAIJRestoreArray(A, &aa));
590: PetscFunctionReturn(PETSC_SUCCESS);
591: }
593: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
594: {
595: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
596: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
597: PetscInt *ai = a->i, *ailen = a->ilen;
598: const MatScalar *ap, *aa;
600: PetscFunctionBegin;
601: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
602: for (k = 0; k < m; k++) { /* loop over rows */
603: row = im[k];
604: if (row < 0) {
605: v += n;
606: continue;
607: } /* negative row */
608: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
609: rp = PetscSafePointerPlusOffset(aj, ai[row]);
610: ap = PetscSafePointerPlusOffset(aa, ai[row]);
611: nrow = ailen[row];
612: for (l = 0; l < n; l++) { /* loop over columns */
613: if (in[l] < 0) {
614: v++;
615: continue;
616: } /* negative column */
617: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
618: col = in[l];
619: high = nrow;
620: low = 0; /* assume unsorted */
621: while (high - low > 5) {
622: t = (low + high) / 2;
623: if (rp[t] > col) high = t;
624: else low = t;
625: }
626: for (i = low; i < high; i++) {
627: if (rp[i] > col) break;
628: if (rp[i] == col) {
629: *v++ = ap[i];
630: goto finished;
631: }
632: }
633: *v++ = 0.0;
634: finished:;
635: }
636: }
637: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
638: PetscFunctionReturn(PETSC_SUCCESS);
639: }
641: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
642: {
643: Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data;
644: const PetscScalar *av;
645: PetscInt header[4], M, N, m, nz, i;
646: PetscInt *rowlens;
648: PetscFunctionBegin;
649: PetscCall(PetscViewerSetUp(viewer));
651: M = mat->rmap->N;
652: N = mat->cmap->N;
653: m = mat->rmap->n;
654: nz = A->nz;
656: /* write matrix header */
657: header[0] = MAT_FILE_CLASSID;
658: header[1] = M;
659: header[2] = N;
660: header[3] = nz;
661: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
663: /* fill in and store row lengths */
664: PetscCall(PetscMalloc1(m, &rowlens));
665: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
666: if (PetscDefined(USE_DEBUG)) {
667: PetscInt mnz = 0;
669: for (i = 0; i < m; i++) mnz += rowlens[i];
670: PetscCheck(nz == mnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row lens %" PetscInt_FMT " do not sum to nz %" PetscInt_FMT, mnz, nz);
671: }
672: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
673: PetscCall(PetscFree(rowlens));
674: /* store column indices */
675: PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
676: /* store nonzero values */
677: PetscCall(MatSeqAIJGetArrayRead(mat, &av));
678: PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
679: PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));
681: /* write block size option to the viewer's .info file */
682: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
683: PetscFunctionReturn(PETSC_SUCCESS);
684: }
686: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
687: {
688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
689: PetscInt i, k, m = A->rmap->N;
691: PetscFunctionBegin;
692: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
693: for (i = 0; i < m; i++) {
694: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
695: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
696: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
697: }
698: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
699: PetscFunctionReturn(PETSC_SUCCESS);
700: }
702: static PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer)
703: {
704: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
705: const PetscScalar *av;
706: PetscInt i, j, m = A->rmap->n;
707: const char *name;
708: PetscViewerFormat format;
710: PetscFunctionBegin;
711: if (A->structure_only) {
712: PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
713: PetscFunctionReturn(PETSC_SUCCESS);
714: }
716: PetscCall(PetscViewerGetFormat(viewer, &format));
717: // By petsc's rule, even PETSC_VIEWER_ASCII_INFO_DETAIL doesn't print matrix entries
718: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);
720: /* trigger copy to CPU if needed */
721: PetscCall(MatSeqAIJGetArrayRead(A, &av));
722: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
723: if (format == PETSC_VIEWER_ASCII_MATLAB) {
724: PetscInt nofinalvalue = 0;
725: if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) {
726: /* Need a dummy value to ensure the dimension of the matrix. */
727: nofinalvalue = 1;
728: }
729: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
730: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n));
731: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz));
732: #if defined(PETSC_USE_COMPLEX)
733: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue));
734: #else
735: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue));
736: #endif
737: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n"));
739: for (i = 0; i < m; i++) {
740: for (j = a->i[i]; j < a->i[i + 1]; j++) {
741: #if defined(PETSC_USE_COMPLEX)
742: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", i + 1, a->j[j] + 1, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
743: #else
744: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j]));
745: #endif
746: }
747: }
748: if (nofinalvalue) {
749: #if defined(PETSC_USE_COMPLEX)
750: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.));
751: #else
752: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0));
753: #endif
754: }
755: PetscCall(PetscObjectGetName((PetscObject)A, &name));
756: PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name));
757: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
758: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
759: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
760: for (i = 0; i < m; i++) {
761: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
762: for (j = a->i[i]; j < a->i[i + 1]; j++) {
763: #if defined(PETSC_USE_COMPLEX)
764: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
765: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
766: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
767: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
768: } else if (PetscRealPart(a->a[j]) != 0.0) {
769: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
770: }
771: #else
772: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
773: #endif
774: }
775: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
776: }
777: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
778: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
779: PetscInt nzd = 0, fshift = 1, *sptr;
780: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
781: PetscCall(PetscMalloc1(m + 1, &sptr));
782: for (i = 0; i < m; i++) {
783: sptr[i] = nzd + 1;
784: for (j = a->i[i]; j < a->i[i + 1]; j++) {
785: if (a->j[j] >= i) {
786: #if defined(PETSC_USE_COMPLEX)
787: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
788: #else
789: if (a->a[j] != 0.0) nzd++;
790: #endif
791: }
792: }
793: }
794: sptr[m] = nzd + 1;
795: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd));
796: for (i = 0; i < m + 1; i += 6) {
797: if (i + 4 < m) {
798: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4], sptr[i + 5]));
799: } else if (i + 3 < m) {
800: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4]));
801: } else if (i + 2 < m) {
802: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3]));
803: } else if (i + 1 < m) {
804: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2]));
805: } else if (i < m) {
806: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1]));
807: } else {
808: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i]));
809: }
810: }
811: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
812: PetscCall(PetscFree(sptr));
813: for (i = 0; i < m; i++) {
814: for (j = a->i[i]; j < a->i[i + 1]; j++) {
815: if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift));
816: }
817: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
818: }
819: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
820: for (i = 0; i < m; i++) {
821: for (j = a->i[i]; j < a->i[i + 1]; j++) {
822: if (a->j[j] >= i) {
823: #if defined(PETSC_USE_COMPLEX)
824: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e %18.16e ", (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
825: #else
826: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j]));
827: #endif
828: }
829: }
830: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
831: }
832: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
833: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
834: PetscInt cnt = 0, jcnt;
835: PetscScalar value;
836: #if defined(PETSC_USE_COMPLEX)
837: PetscBool realonly = PETSC_TRUE;
839: for (i = 0; i < a->i[m]; i++) {
840: if (PetscImaginaryPart(a->a[i]) != 0.0) {
841: realonly = PETSC_FALSE;
842: break;
843: }
844: }
845: #endif
847: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
848: for (i = 0; i < m; i++) {
849: jcnt = 0;
850: for (j = 0; j < A->cmap->n; j++) {
851: if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) {
852: value = a->a[cnt++];
853: jcnt++;
854: } else {
855: value = 0.0;
856: }
857: #if defined(PETSC_USE_COMPLEX)
858: if (realonly) {
859: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value)));
860: } else {
861: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value)));
862: }
863: #else
864: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value));
865: #endif
866: }
867: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
868: }
869: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
870: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
871: PetscInt fshift = 1;
872: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
873: #if defined(PETSC_USE_COMPLEX)
874: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n"));
875: #else
876: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n"));
877: #endif
878: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz));
879: for (i = 0; i < m; i++) {
880: for (j = a->i[i]; j < a->i[i + 1]; j++) {
881: #if defined(PETSC_USE_COMPLEX)
882: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g %g\n", i + fshift, a->j[j] + fshift, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
883: #else
884: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j]));
885: #endif
886: }
887: }
888: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
889: } else {
890: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
891: if (A->factortype) {
892: const PetscInt *adiag;
894: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
895: for (i = 0; i < m; i++) {
896: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
897: /* L part */
898: for (j = a->i[i]; j < a->i[i + 1]; j++) {
899: #if defined(PETSC_USE_COMPLEX)
900: if (PetscImaginaryPart(a->a[j]) > 0.0) {
901: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
902: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
903: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
904: } else {
905: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
906: }
907: #else
908: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
909: #endif
910: }
911: /* diagonal */
912: j = adiag[i];
913: #if defined(PETSC_USE_COMPLEX)
914: if (PetscImaginaryPart(a->a[j]) > 0.0) {
915: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)PetscImaginaryPart(1 / a->a[j])));
916: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
917: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)(-PetscImaginaryPart(1 / a->a[j]))));
918: } else {
919: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1 / a->a[j])));
920: }
921: #else
922: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1 / a->a[j])));
923: #endif
925: /* U part */
926: for (j = adiag[i + 1] + 1; j < adiag[i]; j++) {
927: #if defined(PETSC_USE_COMPLEX)
928: if (PetscImaginaryPart(a->a[j]) > 0.0) {
929: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
930: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
931: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
932: } else {
933: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
934: }
935: #else
936: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
937: #endif
938: }
939: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
940: }
941: } else {
942: for (i = 0; i < m; i++) {
943: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
944: for (j = a->i[i]; j < a->i[i + 1]; j++) {
945: #if defined(PETSC_USE_COMPLEX)
946: if (PetscImaginaryPart(a->a[j]) > 0.0) {
947: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
948: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
949: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
950: } else {
951: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
952: }
953: #else
954: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
955: #endif
956: }
957: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
958: }
959: }
960: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
961: }
962: PetscCall(PetscViewerFlush(viewer));
963: PetscFunctionReturn(PETSC_SUCCESS);
964: }
966: #include <petscdraw.h>
967: static PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
968: {
969: Mat A = (Mat)Aa;
970: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
971: PetscInt i, j, m = A->rmap->n;
972: int color;
973: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
974: PetscViewer viewer;
975: PetscViewerFormat format;
976: const PetscScalar *aa;
978: PetscFunctionBegin;
979: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
980: PetscCall(PetscViewerGetFormat(viewer, &format));
981: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
983: /* loop over matrix elements drawing boxes */
984: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
985: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
986: PetscDrawCollectiveBegin(draw);
987: /* Blue for negative, Cyan for zero and Red for positive */
988: color = PETSC_DRAW_BLUE;
989: for (i = 0; i < m; i++) {
990: y_l = m - i - 1.0;
991: y_r = y_l + 1.0;
992: for (j = a->i[i]; j < a->i[i + 1]; j++) {
993: x_l = a->j[j];
994: x_r = x_l + 1.0;
995: if (PetscRealPart(aa[j]) >= 0.) continue;
996: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
997: }
998: }
999: color = PETSC_DRAW_CYAN;
1000: for (i = 0; i < m; i++) {
1001: y_l = m - i - 1.0;
1002: y_r = y_l + 1.0;
1003: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1004: x_l = a->j[j];
1005: x_r = x_l + 1.0;
1006: if (aa[j] != 0.) continue;
1007: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1008: }
1009: }
1010: color = PETSC_DRAW_RED;
1011: for (i = 0; i < m; i++) {
1012: y_l = m - i - 1.0;
1013: y_r = y_l + 1.0;
1014: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1015: x_l = a->j[j];
1016: x_r = x_l + 1.0;
1017: if (PetscRealPart(aa[j]) <= 0.) continue;
1018: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1019: }
1020: }
1021: PetscDrawCollectiveEnd(draw);
1022: } else {
1023: /* use contour shading to indicate magnitude of values */
1024: /* first determine max of all nonzero values */
1025: PetscReal minv = 0.0, maxv = 0.0;
1026: PetscInt nz = a->nz, count = 0;
1027: PetscDraw popup;
1029: for (i = 0; i < nz; i++) {
1030: if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1031: }
1032: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1033: PetscCall(PetscDrawGetPopup(draw, &popup));
1034: PetscCall(PetscDrawScalePopup(popup, minv, maxv));
1036: PetscDrawCollectiveBegin(draw);
1037: for (i = 0; i < m; i++) {
1038: y_l = m - i - 1.0;
1039: y_r = y_l + 1.0;
1040: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1041: x_l = a->j[j];
1042: x_r = x_l + 1.0;
1043: color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1044: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1045: count++;
1046: }
1047: }
1048: PetscDrawCollectiveEnd(draw);
1049: }
1050: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1051: PetscFunctionReturn(PETSC_SUCCESS);
1052: }
1054: #include <petscdraw.h>
1055: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1056: {
1057: PetscDraw draw;
1058: PetscReal xr, yr, xl, yl, h, w;
1059: PetscBool isnull;
1061: PetscFunctionBegin;
1062: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1063: PetscCall(PetscDrawIsNull(draw, &isnull));
1064: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1066: xr = A->cmap->n;
1067: yr = A->rmap->n;
1068: h = yr / 10.0;
1069: w = xr / 10.0;
1070: xr += w;
1071: yr += h;
1072: xl = -w;
1073: yl = -h;
1074: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1075: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1076: PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1077: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1078: PetscCall(PetscDrawSave(draw));
1079: PetscFunctionReturn(PETSC_SUCCESS);
1080: }
1082: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1083: {
1084: PetscBool isascii, isbinary, isdraw;
1086: PetscFunctionBegin;
1087: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1088: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1089: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1090: if (isascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1091: else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1092: else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1093: PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1094: PetscFunctionReturn(PETSC_SUCCESS);
1095: }
1097: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1098: {
1099: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1100: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1101: PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0;
1102: MatScalar *aa = a->a, *ap;
1103: PetscReal ratio = 0.6;
1105: PetscFunctionBegin;
1106: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
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, e.g., via MatSetOption(A, MAT_USE_INODES, val) */
1109: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode)); /* read the sparsity pattern */
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(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));
1145: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1146: PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));
1148: A->info.mallocs += a->reallocs;
1149: a->reallocs = 0;
1150: A->info.nz_unneeded = (PetscReal)fshift;
1151: a->rmax = rmax;
1153: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1154: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1155: PetscFunctionReturn(PETSC_SUCCESS);
1156: }
1158: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1159: {
1160: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1161: PetscInt i, nz = a->nz;
1162: MatScalar *aa;
1164: PetscFunctionBegin;
1165: PetscCall(MatSeqAIJGetArray(A, &aa));
1166: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1167: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1168: PetscFunctionReturn(PETSC_SUCCESS);
1169: }
1171: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1172: {
1173: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1174: PetscInt i, nz = a->nz;
1175: MatScalar *aa;
1177: PetscFunctionBegin;
1178: PetscCall(MatSeqAIJGetArray(A, &aa));
1179: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1180: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1181: PetscFunctionReturn(PETSC_SUCCESS);
1182: }
1184: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1185: {
1186: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1187: MatScalar *aa;
1189: PetscFunctionBegin;
1190: PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1191: PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1192: PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1193: PetscFunctionReturn(PETSC_SUCCESS);
1194: }
1196: static PetscErrorCode MatReset_SeqAIJ(Mat A)
1197: {
1198: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1200: PetscFunctionBegin;
1201: if (A->hash_active) {
1202: A->ops[0] = a->cops;
1203: PetscCall(PetscHMapIJVDestroy(&a->ht));
1204: PetscCall(PetscFree(a->dnz));
1205: A->hash_active = PETSC_FALSE;
1206: }
1208: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1209: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1210: PetscCall(ISDestroy(&a->row));
1211: PetscCall(ISDestroy(&a->col));
1212: PetscCall(PetscFree(a->diag));
1213: PetscCall(PetscFree(a->ibdiag));
1214: PetscCall(PetscFree(a->imax));
1215: PetscCall(PetscFree(a->ilen));
1216: PetscCall(PetscFree(a->ipre));
1217: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1218: PetscCall(PetscFree(a->solve_work));
1219: PetscCall(ISDestroy(&a->icol));
1220: PetscCall(PetscFree(a->saved_values));
1221: a->compressedrow.use = PETSC_FALSE;
1222: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1223: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1224: PetscFunctionReturn(PETSC_SUCCESS);
1225: }
1227: static PetscErrorCode MatResetHash_SeqAIJ(Mat A)
1228: {
1229: PetscFunctionBegin;
1230: PetscCall(MatReset_SeqAIJ(A));
1231: PetscCall(MatCreate_SeqAIJ_Inode(A));
1232: PetscCall(MatSetUp_Seq_Hash(A));
1233: A->nonzerostate++;
1234: PetscFunctionReturn(PETSC_SUCCESS);
1235: }
1237: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1238: {
1239: PetscFunctionBegin;
1240: PetscCall(MatReset_SeqAIJ(A));
1241: PetscCall(PetscFree(A->data));
1243: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1244: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1245: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1246: users reusing the matrix object with different data to incur in obscure segmentation faults
1247: due to different matrix sizes */
1248: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1250: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1251: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1252: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1253: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1254: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1255: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1256: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1257: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1258: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1259: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1260: #if defined(PETSC_HAVE_MKL_SPARSE)
1261: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1262: #endif
1263: #if defined(PETSC_HAVE_CUDA)
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1265: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1266: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1267: #endif
1268: #if defined(PETSC_HAVE_HIP)
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1270: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1271: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1272: #endif
1273: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1274: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1275: #endif
1276: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1277: #if defined(PETSC_HAVE_ELEMENTAL)
1278: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1279: #endif
1280: #if defined(PETSC_HAVE_SCALAPACK) && (defined(PETSC_USE_REAL_SINGLE) || defined(PETSC_USE_REAL_DOUBLE))
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1282: #endif
1283: #if defined(PETSC_HAVE_HYPRE)
1284: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1285: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1286: #endif
1287: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1288: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1289: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1290: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1291: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1292: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1293: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1294: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetHash_C", NULL));
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1296: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1297: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1302: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1303: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1304: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1305: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1306: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1307: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1308: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1309: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1310: PetscFunctionReturn(PETSC_SUCCESS);
1311: }
1313: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1314: {
1315: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1317: PetscFunctionBegin;
1318: switch (op) {
1319: case MAT_ROW_ORIENTED:
1320: a->roworiented = flg;
1321: break;
1322: case MAT_KEEP_NONZERO_PATTERN:
1323: a->keepnonzeropattern = flg;
1324: break;
1325: case MAT_NEW_NONZERO_LOCATIONS:
1326: a->nonew = (flg ? 0 : 1);
1327: break;
1328: case MAT_NEW_NONZERO_LOCATION_ERR:
1329: a->nonew = (flg ? -1 : 0);
1330: break;
1331: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1332: a->nonew = (flg ? -2 : 0);
1333: break;
1334: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1335: a->nounused = (flg ? -1 : 0);
1336: break;
1337: case MAT_IGNORE_ZERO_ENTRIES:
1338: a->ignorezeroentries = flg;
1339: break;
1340: case MAT_USE_INODES:
1341: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1342: break;
1343: case MAT_SUBMAT_SINGLEIS:
1344: A->submat_singleis = flg;
1345: break;
1346: case MAT_SORTED_FULL:
1347: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1348: else A->ops->setvalues = MatSetValues_SeqAIJ;
1349: break;
1350: case MAT_FORM_EXPLICIT_TRANSPOSE:
1351: A->form_explicit_transpose = flg;
1352: break;
1353: default:
1354: break;
1355: }
1356: PetscFunctionReturn(PETSC_SUCCESS);
1357: }
1359: PETSC_INTERN PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1360: {
1361: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1362: PetscInt n, *ai = a->i;
1363: PetscScalar *x;
1364: const PetscScalar *aa;
1365: const PetscInt *diag;
1366: PetscBool diagDense;
1368: PetscFunctionBegin;
1369: PetscCall(VecGetLocalSize(v, &n));
1370: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1371: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1372: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1373: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, NULL));
1374: PetscCall(VecGetArrayWrite(v, &x));
1375: for (PetscInt i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1376: PetscCall(VecRestoreArrayWrite(v, &x));
1377: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1378: PetscFunctionReturn(PETSC_SUCCESS);
1379: }
1381: PetscCheck(A->factortype == MAT_FACTOR_NONE, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Not for factor matrices that are not ILU or LU");
1382: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, &diagDense));
1383: PetscCall(VecGetArrayWrite(v, &x));
1384: if (diagDense) {
1385: for (PetscInt i = 0; i < n; i++) x[i] = aa[diag[i]];
1386: } else {
1387: for (PetscInt i = 0; i < n; i++) x[i] = (diag[i] == ai[i + 1]) ? 0.0 : aa[diag[i]];
1388: }
1389: PetscCall(VecRestoreArrayWrite(v, &x));
1390: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1391: PetscFunctionReturn(PETSC_SUCCESS);
1392: }
1394: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1395: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1396: {
1397: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1398: const MatScalar *aa;
1399: PetscScalar *y;
1400: const PetscScalar *x;
1401: PetscInt m = A->rmap->n;
1402: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1403: const MatScalar *v;
1404: PetscScalar alpha;
1405: PetscInt n, i, j;
1406: const PetscInt *idx, *ii, *ridx = NULL;
1407: Mat_CompressedRow cprow = a->compressedrow;
1408: PetscBool usecprow = cprow.use;
1409: #endif
1411: PetscFunctionBegin;
1412: if (zz != yy) PetscCall(VecCopy(zz, yy));
1413: PetscCall(VecGetArrayRead(xx, &x));
1414: PetscCall(VecGetArray(yy, &y));
1415: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1417: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1418: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1419: #else
1420: if (usecprow) {
1421: m = cprow.nrows;
1422: ii = cprow.i;
1423: ridx = cprow.rindex;
1424: } else {
1425: ii = a->i;
1426: }
1427: for (i = 0; i < m; i++) {
1428: idx = a->j + ii[i];
1429: v = aa + ii[i];
1430: n = ii[i + 1] - ii[i];
1431: if (usecprow) {
1432: alpha = x[ridx[i]];
1433: } else {
1434: alpha = x[i];
1435: }
1436: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1437: }
1438: #endif
1439: PetscCall(PetscLogFlops(2.0 * a->nz));
1440: PetscCall(VecRestoreArrayRead(xx, &x));
1441: PetscCall(VecRestoreArray(yy, &y));
1442: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1443: PetscFunctionReturn(PETSC_SUCCESS);
1444: }
1446: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1447: {
1448: PetscFunctionBegin;
1449: PetscCall(VecSet(yy, 0.0));
1450: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1451: PetscFunctionReturn(PETSC_SUCCESS);
1452: }
1454: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1456: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1457: {
1458: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1459: PetscScalar *y;
1460: const PetscScalar *x;
1461: const MatScalar *a_a;
1462: PetscInt m = A->rmap->n;
1463: const PetscInt *ii, *ridx = NULL;
1464: PetscBool usecprow = a->compressedrow.use;
1466: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1467: #pragma disjoint(*x, *y, *aa)
1468: #endif
1470: PetscFunctionBegin;
1471: if (a->inode.use && a->inode.checked) {
1472: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1473: PetscFunctionReturn(PETSC_SUCCESS);
1474: }
1475: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1476: PetscCall(VecGetArrayRead(xx, &x));
1477: PetscCall(VecGetArray(yy, &y));
1478: ii = a->i;
1479: if (usecprow) { /* use compressed row format */
1480: PetscCall(PetscArrayzero(y, m));
1481: m = a->compressedrow.nrows;
1482: ii = a->compressedrow.i;
1483: ridx = a->compressedrow.rindex;
1484: PetscPragmaUseOMPKernels(parallel for)
1485: for (PetscInt i = 0; i < m; i++) {
1486: PetscInt n = ii[i + 1] - ii[i];
1487: const PetscInt *aj = a->j + ii[i];
1488: const PetscScalar *aa = a_a + ii[i];
1489: PetscScalar sum = 0.0;
1490: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1491: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1492: y[ridx[i]] = sum;
1493: }
1494: } else { /* do not use compressed row format */
1495: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1496: fortranmultaij_(&m, x, ii, a->j, a_a, y);
1497: #else
1498: PetscPragmaUseOMPKernels(parallel for)
1499: for (PetscInt i = 0; i < m; i++) {
1500: PetscInt n = ii[i + 1] - ii[i];
1501: const PetscInt *aj = a->j + ii[i];
1502: const PetscScalar *aa = a_a + ii[i];
1503: PetscScalar sum = 0.0;
1504: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1505: y[i] = sum;
1506: }
1507: #endif
1508: }
1509: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1510: PetscCall(VecRestoreArrayRead(xx, &x));
1511: PetscCall(VecRestoreArray(yy, &y));
1512: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1513: PetscFunctionReturn(PETSC_SUCCESS);
1514: }
1516: // HACK!!!!! Used by src/mat/tests/ex170.c
1517: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1518: {
1519: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1520: PetscScalar *y;
1521: const PetscScalar *x;
1522: const MatScalar *aa, *a_a;
1523: PetscInt m = A->rmap->n;
1524: const PetscInt *aj, *ii, *ridx = NULL;
1525: PetscInt n, i, nonzerorow = 0;
1526: PetscScalar sum;
1527: PetscBool usecprow = a->compressedrow.use;
1529: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1530: #pragma disjoint(*x, *y, *aa)
1531: #endif
1533: PetscFunctionBegin;
1534: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1535: PetscCall(VecGetArrayRead(xx, &x));
1536: PetscCall(VecGetArray(yy, &y));
1537: if (usecprow) { /* use compressed row format */
1538: m = a->compressedrow.nrows;
1539: ii = a->compressedrow.i;
1540: ridx = a->compressedrow.rindex;
1541: for (i = 0; i < m; i++) {
1542: n = ii[i + 1] - ii[i];
1543: aj = a->j + ii[i];
1544: aa = a_a + ii[i];
1545: sum = 0.0;
1546: nonzerorow += (n > 0);
1547: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1548: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1549: y[*ridx++] = sum;
1550: }
1551: } else { /* do not use compressed row format */
1552: ii = a->i;
1553: for (i = 0; i < m; i++) {
1554: n = ii[i + 1] - ii[i];
1555: aj = a->j + ii[i];
1556: aa = a_a + ii[i];
1557: sum = 0.0;
1558: nonzerorow += (n > 0);
1559: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1560: y[i] = sum;
1561: }
1562: }
1563: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1564: PetscCall(VecRestoreArrayRead(xx, &x));
1565: PetscCall(VecRestoreArray(yy, &y));
1566: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1567: PetscFunctionReturn(PETSC_SUCCESS);
1568: }
1570: // HACK!!!!! Used by src/mat/tests/ex170.c
1571: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1572: {
1573: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1574: PetscScalar *y, *z;
1575: const PetscScalar *x;
1576: const MatScalar *aa, *a_a;
1577: PetscInt m = A->rmap->n, *aj, *ii;
1578: PetscInt n, i, *ridx = NULL;
1579: PetscScalar sum;
1580: PetscBool usecprow = a->compressedrow.use;
1582: PetscFunctionBegin;
1583: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1584: PetscCall(VecGetArrayRead(xx, &x));
1585: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1586: if (usecprow) { /* use compressed row format */
1587: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1588: m = a->compressedrow.nrows;
1589: ii = a->compressedrow.i;
1590: ridx = a->compressedrow.rindex;
1591: for (i = 0; i < m; i++) {
1592: n = ii[i + 1] - ii[i];
1593: aj = a->j + ii[i];
1594: aa = a_a + ii[i];
1595: sum = y[*ridx];
1596: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1597: z[*ridx++] = sum;
1598: }
1599: } else { /* do not use compressed row format */
1600: ii = a->i;
1601: for (i = 0; i < m; i++) {
1602: n = ii[i + 1] - ii[i];
1603: aj = a->j + ii[i];
1604: aa = a_a + ii[i];
1605: sum = y[i];
1606: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1607: z[i] = sum;
1608: }
1609: }
1610: PetscCall(PetscLogFlops(2.0 * a->nz));
1611: PetscCall(VecRestoreArrayRead(xx, &x));
1612: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1613: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1614: PetscFunctionReturn(PETSC_SUCCESS);
1615: }
1617: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1618: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1619: {
1620: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1621: PetscScalar *y, *z;
1622: const PetscScalar *x;
1623: const MatScalar *a_a;
1624: const PetscInt *ii, *ridx = NULL;
1625: PetscInt m = A->rmap->n;
1626: PetscBool usecprow = a->compressedrow.use;
1628: PetscFunctionBegin;
1629: if (a->inode.use && a->inode.checked) {
1630: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1631: PetscFunctionReturn(PETSC_SUCCESS);
1632: }
1633: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1634: PetscCall(VecGetArrayRead(xx, &x));
1635: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1636: if (usecprow) { /* use compressed row format */
1637: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1638: m = a->compressedrow.nrows;
1639: ii = a->compressedrow.i;
1640: ridx = a->compressedrow.rindex;
1641: for (PetscInt i = 0; i < m; i++) {
1642: PetscInt n = ii[i + 1] - ii[i];
1643: const PetscInt *aj = a->j + ii[i];
1644: const PetscScalar *aa = a_a + ii[i];
1645: PetscScalar sum = y[*ridx];
1646: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1647: z[*ridx++] = sum;
1648: }
1649: } else { /* do not use compressed row format */
1650: ii = a->i;
1651: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1652: fortranmultaddaij_(&m, x, ii, a->j, a_a, y, z);
1653: #else
1654: PetscPragmaUseOMPKernels(parallel for)
1655: for (PetscInt i = 0; i < m; i++) {
1656: PetscInt n = ii[i + 1] - ii[i];
1657: const PetscInt *aj = a->j + ii[i];
1658: const PetscScalar *aa = a_a + ii[i];
1659: PetscScalar sum = y[i];
1660: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1661: z[i] = sum;
1662: }
1663: #endif
1664: }
1665: PetscCall(PetscLogFlops(2.0 * a->nz));
1666: PetscCall(VecRestoreArrayRead(xx, &x));
1667: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1668: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1669: PetscFunctionReturn(PETSC_SUCCESS);
1670: }
1672: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1673: {
1674: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1675: const PetscInt *diag;
1676: const PetscInt *ii = (const PetscInt *)a->i;
1677: PetscBool diagDense;
1679: PetscFunctionBegin;
1680: if (!A->preallocated || !a->nz) {
1681: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1682: PetscCall(MatShift_Basic(A, v));
1683: PetscFunctionReturn(PETSC_SUCCESS);
1684: }
1686: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, &diagDense));
1687: if (diagDense) {
1688: PetscScalar *Aa;
1690: PetscCall(MatSeqAIJGetArray(A, &Aa));
1691: for (PetscInt i = 0; i < A->rmap->n; i++) Aa[diag[i]] += v;
1692: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
1693: } else {
1694: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1695: PetscInt *oldj = a->j, *oldi = a->i;
1696: PetscBool free_a = a->free_a, free_ij = a->free_ij;
1697: const PetscScalar *Aa;
1698: PetscInt *mdiag = NULL;
1700: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1701: for (PetscInt i = 0; i < A->rmap->n; i++) {
1702: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1703: mdiag[i] = 1;
1704: }
1705: }
1706: PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1707: PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));
1709: a->a = NULL;
1710: a->j = NULL;
1711: a->i = NULL;
1712: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1713: for (PetscInt i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1714: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1716: /* copy old values into new matrix data structure */
1717: for (PetscInt i = 0; i < A->rmap->n; i++) {
1718: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1719: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1720: }
1721: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1722: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1723: if (free_a) PetscCall(PetscShmgetDeallocateArray((void **)&olda));
1724: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldj));
1725: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldi));
1726: PetscCall(PetscFree(mdiag));
1727: }
1728: PetscFunctionReturn(PETSC_SUCCESS);
1729: }
1731: #include <petscblaslapack.h>
1732: #include <petsc/private/kernels/blockinvert.h>
1734: /*
1735: Note that values is allocated externally by the PC and then passed into this routine
1736: */
1737: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1738: {
1739: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1740: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1741: const PetscReal shift = 0.0;
1742: PetscInt ipvt[5];
1743: PetscCount flops = 0;
1744: PetscScalar work[25], *v_work;
1746: PetscFunctionBegin;
1747: allowzeropivot = PetscNot(A->erroriffailure);
1748: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1749: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1750: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1751: PetscCall(PetscMalloc1(bsizemax, &indx));
1752: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1753: ncnt = 0;
1754: for (i = 0; i < nblocks; i++) {
1755: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1756: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1757: switch (bsizes[i]) {
1758: case 1:
1759: *diag = 1.0 / (*diag);
1760: break;
1761: case 2:
1762: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1763: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1764: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1765: break;
1766: case 3:
1767: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1768: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1769: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1770: break;
1771: case 4:
1772: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1773: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1774: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1775: break;
1776: case 5:
1777: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1778: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1779: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1780: break;
1781: case 6:
1782: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1783: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1784: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1785: break;
1786: case 7:
1787: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1788: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1789: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1790: break;
1791: default:
1792: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1793: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1794: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1795: }
1796: ncnt += bsizes[i];
1797: diag += bsizes[i] * bsizes[i];
1798: flops += 2 * PetscPowInt64(bsizes[i], 3) / 3;
1799: }
1800: PetscCall(PetscLogFlops(flops));
1801: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1802: PetscCall(PetscFree(indx));
1803: PetscFunctionReturn(PETSC_SUCCESS);
1804: }
1806: /*
1807: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1808: */
1809: static PetscErrorCode MatInvertDiagonalForSOR_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1810: {
1811: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1812: PetscInt i, m = A->rmap->n;
1813: const MatScalar *v;
1814: PetscScalar *idiag, *mdiag;
1815: PetscBool diagDense;
1816: const PetscInt *diag;
1818: PetscFunctionBegin;
1819: if (a->idiagState == ((PetscObject)A)->state && a->omega == omega && a->fshift == fshift) PetscFunctionReturn(PETSC_SUCCESS);
1820: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, &diagDense));
1821: PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix must have all diagonal locations to invert them");
1822: if (!a->idiag) PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work));
1824: mdiag = a->mdiag;
1825: idiag = a->idiag;
1826: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1827: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1828: for (i = 0; i < m; i++) {
1829: mdiag[i] = v[diag[i]];
1830: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1831: PetscCheck(PetscRealPart(fshift), PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1832: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1833: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1834: A->factorerror_zeropivot_value = 0.0;
1835: A->factorerror_zeropivot_row = i;
1836: }
1837: idiag[i] = 1.0 / v[diag[i]];
1838: }
1839: PetscCall(PetscLogFlops(m));
1840: } else {
1841: for (i = 0; i < m; i++) {
1842: mdiag[i] = v[diag[i]];
1843: idiag[i] = omega / (fshift + v[diag[i]]);
1844: }
1845: PetscCall(PetscLogFlops(2.0 * m));
1846: }
1847: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1848: a->idiagState = ((PetscObject)A)->state;
1849: a->omega = omega;
1850: a->fshift = fshift;
1851: PetscFunctionReturn(PETSC_SUCCESS);
1852: }
1854: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1855: {
1856: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1857: PetscScalar *x, d, sum, *t, scale;
1858: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1859: const PetscScalar *b, *bs, *xb, *ts;
1860: PetscInt n, m = A->rmap->n, i;
1861: const PetscInt *idx, *diag;
1863: PetscFunctionBegin;
1864: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1865: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1866: PetscFunctionReturn(PETSC_SUCCESS);
1867: }
1868: its = its * lits;
1869: PetscCall(MatInvertDiagonalForSOR_SeqAIJ(A, omega, fshift));
1870: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, NULL));
1871: t = a->ssor_work;
1872: idiag = a->idiag;
1873: mdiag = a->mdiag;
1875: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1876: PetscCall(VecGetArray(xx, &x));
1877: PetscCall(VecGetArrayRead(bb, &b));
1878: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1879: if (flag == SOR_APPLY_UPPER) {
1880: /* apply (U + D/omega) to the vector */
1881: bs = b;
1882: for (i = 0; i < m; i++) {
1883: d = fshift + mdiag[i];
1884: n = a->i[i + 1] - diag[i] - 1;
1885: idx = a->j + diag[i] + 1;
1886: v = aa + diag[i] + 1;
1887: sum = b[i] * d / omega;
1888: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1889: x[i] = sum;
1890: }
1891: PetscCall(VecRestoreArray(xx, &x));
1892: PetscCall(VecRestoreArrayRead(bb, &b));
1893: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1894: PetscCall(PetscLogFlops(a->nz));
1895: PetscFunctionReturn(PETSC_SUCCESS);
1896: }
1898: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1899: if (flag & SOR_EISENSTAT) {
1900: /* Let A = L + U + D; where L is lower triangular,
1901: U is upper triangular, E = D/omega; This routine applies
1903: (L + E)^{-1} A (U + E)^{-1}
1905: to a vector efficiently using Eisenstat's trick.
1906: */
1907: scale = (2.0 / omega) - 1.0;
1909: /* x = (E + U)^{-1} b */
1910: for (i = m - 1; i >= 0; i--) {
1911: n = a->i[i + 1] - diag[i] - 1;
1912: idx = a->j + diag[i] + 1;
1913: v = aa + diag[i] + 1;
1914: sum = b[i];
1915: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1916: x[i] = sum * idiag[i];
1917: }
1919: /* t = b - (2*E - D)x */
1920: v = aa;
1921: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
1923: /* t = (E + L)^{-1}t */
1924: ts = t;
1925: diag = a->diag;
1926: for (i = 0; i < m; i++) {
1927: n = diag[i] - a->i[i];
1928: idx = a->j + a->i[i];
1929: v = aa + a->i[i];
1930: sum = t[i];
1931: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
1932: t[i] = sum * idiag[i];
1933: /* x = x + t */
1934: x[i] += t[i];
1935: }
1937: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
1938: PetscCall(VecRestoreArray(xx, &x));
1939: PetscCall(VecRestoreArrayRead(bb, &b));
1940: PetscFunctionReturn(PETSC_SUCCESS);
1941: }
1942: if (flag & SOR_ZERO_INITIAL_GUESS) {
1943: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1944: for (i = 0; i < m; i++) {
1945: n = diag[i] - a->i[i];
1946: idx = a->j + a->i[i];
1947: v = aa + a->i[i];
1948: sum = b[i];
1949: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1950: t[i] = sum;
1951: x[i] = sum * idiag[i];
1952: }
1953: xb = t;
1954: PetscCall(PetscLogFlops(a->nz));
1955: } else xb = b;
1956: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1957: for (i = m - 1; i >= 0; i--) {
1958: n = a->i[i + 1] - diag[i] - 1;
1959: idx = a->j + diag[i] + 1;
1960: v = aa + diag[i] + 1;
1961: sum = xb[i];
1962: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1963: if (xb == b) {
1964: x[i] = sum * idiag[i];
1965: } else {
1966: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
1967: }
1968: }
1969: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
1970: }
1971: its--;
1972: }
1973: while (its--) {
1974: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1975: for (i = 0; i < m; i++) {
1976: /* lower */
1977: n = diag[i] - a->i[i];
1978: idx = a->j + a->i[i];
1979: v = aa + a->i[i];
1980: sum = b[i];
1981: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1982: t[i] = sum; /* save application of the lower-triangular part */
1983: /* upper */
1984: n = a->i[i + 1] - diag[i] - 1;
1985: idx = a->j + diag[i] + 1;
1986: v = aa + diag[i] + 1;
1987: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1988: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
1989: }
1990: xb = t;
1991: PetscCall(PetscLogFlops(2.0 * a->nz));
1992: } else xb = b;
1993: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1994: for (i = m - 1; i >= 0; i--) {
1995: sum = xb[i];
1996: if (xb == b) {
1997: /* whole matrix (no checkpointing available) */
1998: n = a->i[i + 1] - a->i[i];
1999: idx = a->j + a->i[i];
2000: v = aa + a->i[i];
2001: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2002: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2003: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2004: n = a->i[i + 1] - diag[i] - 1;
2005: idx = a->j + diag[i] + 1;
2006: v = aa + diag[i] + 1;
2007: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2008: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2009: }
2010: }
2011: if (xb == b) {
2012: PetscCall(PetscLogFlops(2.0 * a->nz));
2013: } else {
2014: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2015: }
2016: }
2017: }
2018: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2019: PetscCall(VecRestoreArray(xx, &x));
2020: PetscCall(VecRestoreArrayRead(bb, &b));
2021: PetscFunctionReturn(PETSC_SUCCESS);
2022: }
2024: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2025: {
2026: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2028: PetscFunctionBegin;
2029: info->block_size = 1.0;
2030: info->nz_allocated = a->maxnz;
2031: info->nz_used = a->nz;
2032: info->nz_unneeded = (a->maxnz - a->nz);
2033: info->assemblies = A->num_ass;
2034: info->mallocs = A->info.mallocs;
2035: info->memory = 0; /* REVIEW ME */
2036: if (A->factortype) {
2037: info->fill_ratio_given = A->info.fill_ratio_given;
2038: info->fill_ratio_needed = A->info.fill_ratio_needed;
2039: info->factor_mallocs = A->info.factor_mallocs;
2040: } else {
2041: info->fill_ratio_given = 0;
2042: info->fill_ratio_needed = 0;
2043: info->factor_mallocs = 0;
2044: }
2045: PetscFunctionReturn(PETSC_SUCCESS);
2046: }
2048: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diagv, Vec x, Vec b)
2049: {
2050: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2051: PetscInt i, m = A->rmap->n - 1;
2052: const PetscScalar *xx;
2053: PetscScalar *bb, *aa;
2054: PetscInt d = 0;
2055: const PetscInt *diag;
2057: PetscFunctionBegin;
2058: if (x && b) {
2059: PetscCall(VecGetArrayRead(x, &xx));
2060: PetscCall(VecGetArray(b, &bb));
2061: for (i = 0; i < N; i++) {
2062: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2063: if (rows[i] >= A->cmap->n) continue;
2064: bb[rows[i]] = diagv * xx[rows[i]];
2065: }
2066: PetscCall(VecRestoreArrayRead(x, &xx));
2067: PetscCall(VecRestoreArray(b, &bb));
2068: }
2070: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, NULL));
2071: PetscCall(MatSeqAIJGetArray(A, &aa));
2072: if (a->keepnonzeropattern) {
2073: for (i = 0; i < N; i++) {
2074: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2075: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2076: }
2077: if (diagv != 0.0) {
2078: for (i = 0; i < N; i++) {
2079: d = rows[i];
2080: if (d >= A->cmap->n) continue;
2081: PetscCheck(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);
2082: aa[diag[d]] = diagv;
2083: }
2084: }
2085: } else {
2086: if (diagv != 0.0) {
2087: for (i = 0; i < N; i++) {
2088: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2089: if (a->ilen[rows[i]] > 0) {
2090: if (rows[i] >= A->cmap->n) {
2091: a->ilen[rows[i]] = 0;
2092: } else {
2093: a->ilen[rows[i]] = 1;
2094: aa[a->i[rows[i]]] = diagv;
2095: a->j[a->i[rows[i]]] = rows[i];
2096: }
2097: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2098: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diagv, INSERT_VALUES));
2099: }
2100: }
2101: } else {
2102: for (i = 0; i < N; i++) {
2103: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2104: a->ilen[rows[i]] = 0;
2105: }
2106: }
2107: A->nonzerostate++;
2108: }
2109: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2110: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2111: PetscFunctionReturn(PETSC_SUCCESS);
2112: }
2114: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diagv, Vec x, Vec b)
2115: {
2116: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2117: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2118: PetscBool *zeroed, vecs = PETSC_FALSE;
2119: const PetscScalar *xx;
2120: PetscScalar *bb, *aa;
2121: const PetscInt *diag;
2122: PetscBool diagDense;
2124: PetscFunctionBegin;
2125: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2126: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &diag, &diagDense));
2127: PetscCall(MatSeqAIJGetArray(A, &aa));
2128: if (x && b) {
2129: PetscCall(VecGetArrayRead(x, &xx));
2130: PetscCall(VecGetArray(b, &bb));
2131: vecs = PETSC_TRUE;
2132: }
2133: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2134: for (i = 0; i < N; i++) {
2135: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2136: PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));
2138: zeroed[rows[i]] = PETSC_TRUE;
2139: }
2140: for (i = 0; i < A->rmap->n; i++) {
2141: if (!zeroed[i]) {
2142: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2143: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2144: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2145: aa[j] = 0.0;
2146: }
2147: }
2148: } else if (vecs && i < A->cmap->N) bb[i] = diagv * xx[i];
2149: }
2150: if (x && b) {
2151: PetscCall(VecRestoreArrayRead(x, &xx));
2152: PetscCall(VecRestoreArray(b, &bb));
2153: }
2154: PetscCall(PetscFree(zeroed));
2155: if (diagv != 0.0) {
2156: if (!diagDense) {
2157: for (i = 0; i < N; i++) {
2158: if (rows[i] >= A->cmap->N) continue;
2159: 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]);
2160: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diagv, INSERT_VALUES));
2161: }
2162: } else {
2163: for (i = 0; i < N; i++) aa[diag[rows[i]]] = diagv;
2164: }
2165: }
2166: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2167: if (!diagDense) PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2168: PetscFunctionReturn(PETSC_SUCCESS);
2169: }
2171: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2172: {
2173: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2174: const PetscScalar *aa;
2176: PetscFunctionBegin;
2177: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2178: *nz = a->i[row + 1] - a->i[row];
2179: if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2180: if (idx) {
2181: if (*nz && a->j) *idx = a->j + a->i[row];
2182: else *idx = NULL;
2183: }
2184: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2185: PetscFunctionReturn(PETSC_SUCCESS);
2186: }
2188: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2189: {
2190: PetscFunctionBegin;
2191: PetscFunctionReturn(PETSC_SUCCESS);
2192: }
2194: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2195: {
2196: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2197: const MatScalar *v;
2198: PetscReal sum = 0.0;
2199: PetscInt i, j;
2201: PetscFunctionBegin;
2202: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2203: if (type == NORM_FROBENIUS) {
2204: #if defined(PETSC_USE_REAL___FP16)
2205: PetscBLASInt one = 1, nz = a->nz;
2206: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2207: #else
2208: for (i = 0; i < a->nz; i++) {
2209: sum += PetscRealPart(PetscConj(*v) * (*v));
2210: v++;
2211: }
2212: *nrm = PetscSqrtReal(sum);
2213: #endif
2214: PetscCall(PetscLogFlops(2.0 * a->nz));
2215: } else if (type == NORM_1) {
2216: PetscReal *tmp;
2217: PetscInt *jj = a->j;
2218: PetscCall(PetscCalloc1(A->cmap->n, &tmp));
2219: *nrm = 0.0;
2220: for (j = 0; j < a->nz; j++) {
2221: tmp[*jj++] += PetscAbsScalar(*v);
2222: v++;
2223: }
2224: for (j = 0; j < A->cmap->n; j++) {
2225: if (tmp[j] > *nrm) *nrm = tmp[j];
2226: }
2227: PetscCall(PetscFree(tmp));
2228: PetscCall(PetscLogFlops(a->nz));
2229: } else if (type == NORM_INFINITY) {
2230: *nrm = 0.0;
2231: for (j = 0; j < A->rmap->n; j++) {
2232: const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2233: sum = 0.0;
2234: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2235: sum += PetscAbsScalar(*v2);
2236: v2++;
2237: }
2238: if (sum > *nrm) *nrm = sum;
2239: }
2240: PetscCall(PetscLogFlops(a->nz));
2241: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2242: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2243: PetscFunctionReturn(PETSC_SUCCESS);
2244: }
2246: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2247: {
2248: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2249: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2250: const MatScalar *va, *vb;
2251: PetscInt ma, na, mb, nb, i;
2253: PetscFunctionBegin;
2254: PetscCall(MatGetSize(A, &ma, &na));
2255: PetscCall(MatGetSize(B, &mb, &nb));
2256: if (ma != nb || na != mb) {
2257: *f = PETSC_FALSE;
2258: PetscFunctionReturn(PETSC_SUCCESS);
2259: }
2260: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2261: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2262: aii = aij->i;
2263: bii = bij->i;
2264: adx = aij->j;
2265: bdx = bij->j;
2266: PetscCall(PetscMalloc1(ma, &aptr));
2267: PetscCall(PetscMalloc1(mb, &bptr));
2268: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2269: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2271: *f = PETSC_TRUE;
2272: for (i = 0; i < ma; i++) {
2273: while (aptr[i] < aii[i + 1]) {
2274: PetscInt idc, idr;
2275: PetscScalar vc, vr;
2276: /* column/row index/value */
2277: idc = adx[aptr[i]];
2278: idr = bdx[bptr[idc]];
2279: vc = va[aptr[i]];
2280: vr = vb[bptr[idc]];
2281: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2282: *f = PETSC_FALSE;
2283: goto done;
2284: } else {
2285: aptr[i]++;
2286: if (B || i != idc) bptr[idc]++;
2287: }
2288: }
2289: }
2290: done:
2291: PetscCall(PetscFree(aptr));
2292: PetscCall(PetscFree(bptr));
2293: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2294: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2295: PetscFunctionReturn(PETSC_SUCCESS);
2296: }
2298: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2299: {
2300: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2301: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2302: MatScalar *va, *vb;
2303: PetscInt ma, na, mb, nb, i;
2305: PetscFunctionBegin;
2306: PetscCall(MatGetSize(A, &ma, &na));
2307: PetscCall(MatGetSize(B, &mb, &nb));
2308: if (ma != nb || na != mb) {
2309: *f = PETSC_FALSE;
2310: PetscFunctionReturn(PETSC_SUCCESS);
2311: }
2312: aii = aij->i;
2313: bii = bij->i;
2314: adx = aij->j;
2315: bdx = bij->j;
2316: va = aij->a;
2317: vb = bij->a;
2318: PetscCall(PetscMalloc1(ma, &aptr));
2319: PetscCall(PetscMalloc1(mb, &bptr));
2320: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2321: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2323: *f = PETSC_TRUE;
2324: for (i = 0; i < ma; i++) {
2325: while (aptr[i] < aii[i + 1]) {
2326: PetscInt idc, idr;
2327: PetscScalar vc, vr;
2328: /* column/row index/value */
2329: idc = adx[aptr[i]];
2330: idr = bdx[bptr[idc]];
2331: vc = va[aptr[i]];
2332: vr = vb[bptr[idc]];
2333: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2334: *f = PETSC_FALSE;
2335: goto done;
2336: } else {
2337: aptr[i]++;
2338: if (B || i != idc) bptr[idc]++;
2339: }
2340: }
2341: }
2342: done:
2343: PetscCall(PetscFree(aptr));
2344: PetscCall(PetscFree(bptr));
2345: PetscFunctionReturn(PETSC_SUCCESS);
2346: }
2348: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2349: {
2350: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2351: const PetscScalar *l, *r;
2352: PetscScalar x;
2353: MatScalar *v;
2354: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2355: const PetscInt *jj;
2357: PetscFunctionBegin;
2358: if (ll) {
2359: /* The local size is used so that VecMPI can be passed to this routine
2360: by MatDiagonalScale_MPIAIJ */
2361: PetscCall(VecGetLocalSize(ll, &m));
2362: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2363: PetscCall(VecGetArrayRead(ll, &l));
2364: PetscCall(MatSeqAIJGetArray(A, &v));
2365: for (i = 0; i < m; i++) {
2366: x = l[i];
2367: M = a->i[i + 1] - a->i[i];
2368: for (j = 0; j < M; j++) (*v++) *= x;
2369: }
2370: PetscCall(VecRestoreArrayRead(ll, &l));
2371: PetscCall(PetscLogFlops(nz));
2372: PetscCall(MatSeqAIJRestoreArray(A, &v));
2373: }
2374: if (rr) {
2375: PetscCall(VecGetLocalSize(rr, &n));
2376: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2377: PetscCall(VecGetArrayRead(rr, &r));
2378: PetscCall(MatSeqAIJGetArray(A, &v));
2379: jj = a->j;
2380: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2381: PetscCall(MatSeqAIJRestoreArray(A, &v));
2382: PetscCall(VecRestoreArrayRead(rr, &r));
2383: PetscCall(PetscLogFlops(nz));
2384: }
2385: PetscFunctionReturn(PETSC_SUCCESS);
2386: }
2388: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2389: {
2390: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2391: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2392: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2393: const PetscInt *irow, *icol;
2394: const PetscScalar *aa;
2395: PetscInt nrows, ncols;
2396: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2397: MatScalar *a_new, *mat_a, *c_a;
2398: Mat C;
2399: PetscBool stride;
2401: PetscFunctionBegin;
2402: PetscCall(ISGetIndices(isrow, &irow));
2403: PetscCall(ISGetLocalSize(isrow, &nrows));
2404: PetscCall(ISGetLocalSize(iscol, &ncols));
2406: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2407: if (stride) {
2408: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2409: } else {
2410: first = 0;
2411: step = 0;
2412: }
2413: if (stride && step == 1) {
2414: /* special case of contiguous rows */
2415: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2416: /* loop over new rows determining lens and starting points */
2417: for (i = 0; i < nrows; i++) {
2418: kstart = ai[irow[i]];
2419: kend = kstart + ailen[irow[i]];
2420: starts[i] = kstart;
2421: for (k = kstart; k < kend; k++) {
2422: if (aj[k] >= first) {
2423: starts[i] = k;
2424: break;
2425: }
2426: }
2427: sum = 0;
2428: while (k < kend) {
2429: if (aj[k++] >= first + ncols) break;
2430: sum++;
2431: }
2432: lens[i] = sum;
2433: }
2434: /* create submatrix */
2435: if (scall == MAT_REUSE_MATRIX) {
2436: PetscInt n_cols, n_rows;
2437: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2438: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2439: PetscCall(MatZeroEntries(*B));
2440: C = *B;
2441: } else {
2442: PetscInt rbs, cbs;
2443: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2444: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2445: PetscCall(ISGetBlockSize(isrow, &rbs));
2446: PetscCall(ISGetBlockSize(iscol, &cbs));
2447: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2448: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2449: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2450: }
2451: c = (Mat_SeqAIJ *)C->data;
2453: /* loop over rows inserting into submatrix */
2454: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2455: j_new = c->j;
2456: i_new = c->i;
2457: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2458: for (i = 0; i < nrows; i++) {
2459: ii = starts[i];
2460: lensi = lens[i];
2461: if (lensi) {
2462: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2463: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2464: a_new += lensi;
2465: }
2466: i_new[i + 1] = i_new[i] + lensi;
2467: c->ilen[i] = lensi;
2468: }
2469: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2470: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2471: PetscCall(PetscFree2(lens, starts));
2472: } else {
2473: PetscCall(ISGetIndices(iscol, &icol));
2474: PetscCall(PetscCalloc1(oldcols, &smap));
2475: PetscCall(PetscMalloc1(1 + nrows, &lens));
2476: for (i = 0; i < ncols; i++) {
2477: 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);
2478: smap[icol[i]] = i + 1;
2479: }
2481: /* determine lens of each row */
2482: for (i = 0; i < nrows; i++) {
2483: kstart = ai[irow[i]];
2484: kend = kstart + a->ilen[irow[i]];
2485: lens[i] = 0;
2486: for (k = kstart; k < kend; k++) {
2487: if (smap[aj[k]]) lens[i]++;
2488: }
2489: }
2490: /* Create and fill new matrix */
2491: if (scall == MAT_REUSE_MATRIX) {
2492: PetscBool equal;
2494: c = (Mat_SeqAIJ *)((*B)->data);
2495: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2496: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2497: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2498: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2499: C = *B;
2500: } else {
2501: PetscInt rbs, cbs;
2502: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2503: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2504: PetscCall(ISGetBlockSize(isrow, &rbs));
2505: PetscCall(ISGetBlockSize(iscol, &cbs));
2506: if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2507: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2508: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2509: }
2510: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2512: c = (Mat_SeqAIJ *)C->data;
2513: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2514: for (i = 0; i < nrows; i++) {
2515: row = irow[i];
2516: kstart = ai[row];
2517: kend = kstart + a->ilen[row];
2518: mat_i = c->i[i];
2519: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2520: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2521: mat_ilen = c->ilen + i;
2522: for (k = kstart; k < kend; k++) {
2523: if ((tcol = smap[a->j[k]])) {
2524: *mat_j++ = tcol - 1;
2525: *mat_a++ = aa[k];
2526: (*mat_ilen)++;
2527: }
2528: }
2529: }
2530: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2531: /* Free work space */
2532: PetscCall(ISRestoreIndices(iscol, &icol));
2533: PetscCall(PetscFree(smap));
2534: PetscCall(PetscFree(lens));
2535: /* sort */
2536: for (i = 0; i < nrows; i++) {
2537: PetscInt ilen;
2539: mat_i = c->i[i];
2540: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2541: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2542: ilen = c->ilen[i];
2543: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2544: }
2545: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2546: }
2547: #if defined(PETSC_HAVE_DEVICE)
2548: PetscCall(MatBindToCPU(C, A->boundtocpu));
2549: #endif
2550: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2551: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2553: PetscCall(ISRestoreIndices(isrow, &irow));
2554: *B = C;
2555: PetscFunctionReturn(PETSC_SUCCESS);
2556: }
2558: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2559: {
2560: Mat B;
2562: PetscFunctionBegin;
2563: if (scall == MAT_INITIAL_MATRIX) {
2564: PetscCall(MatCreate(subComm, &B));
2565: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2566: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2567: PetscCall(MatSetType(B, MATSEQAIJ));
2568: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2569: *subMat = B;
2570: } else {
2571: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2572: }
2573: PetscFunctionReturn(PETSC_SUCCESS);
2574: }
2576: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2577: {
2578: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2579: Mat outA;
2580: PetscBool row_identity, col_identity;
2582: PetscFunctionBegin;
2583: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2585: PetscCall(ISIdentity(row, &row_identity));
2586: PetscCall(ISIdentity(col, &col_identity));
2588: outA = inA;
2589: PetscCall(PetscFree(inA->solvertype));
2590: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2592: PetscCall(PetscObjectReference((PetscObject)row));
2593: PetscCall(ISDestroy(&a->row));
2595: a->row = row;
2597: PetscCall(PetscObjectReference((PetscObject)col));
2598: PetscCall(ISDestroy(&a->col));
2600: a->col = col;
2602: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2603: PetscCall(ISDestroy(&a->icol));
2604: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2606: if (!a->solve_work) { /* this matrix may have been factored before */
2607: PetscCall(PetscMalloc1(inA->rmap->n, &a->solve_work));
2608: }
2610: if (row_identity && col_identity) {
2611: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2612: } else {
2613: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2614: }
2615: outA->factortype = MAT_FACTOR_LU;
2616: PetscFunctionReturn(PETSC_SUCCESS);
2617: }
2619: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2620: {
2621: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2622: PetscScalar *v;
2623: PetscBLASInt one = 1, bnz;
2625: PetscFunctionBegin;
2626: PetscCall(MatSeqAIJGetArray(inA, &v));
2627: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2628: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2629: PetscCall(PetscLogFlops(a->nz));
2630: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2631: PetscFunctionReturn(PETSC_SUCCESS);
2632: }
2634: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2635: {
2636: PetscInt i;
2638: PetscFunctionBegin;
2639: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2640: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2642: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2643: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2645: if (submatj->rbuf1) {
2646: PetscCall(PetscFree(submatj->rbuf1[0]));
2647: PetscCall(PetscFree(submatj->rbuf1));
2648: }
2650: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2651: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2652: PetscCall(PetscFree(submatj->pa));
2653: }
2655: #if defined(PETSC_USE_CTABLE)
2656: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2657: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2658: PetscCall(PetscFree(submatj->rmap_loc));
2659: #else
2660: PetscCall(PetscFree(submatj->rmap));
2661: #endif
2663: if (!submatj->allcolumns) {
2664: #if defined(PETSC_USE_CTABLE)
2665: PetscCall(PetscHMapIDestroy(&submatj->cmap));
2666: #else
2667: PetscCall(PetscFree(submatj->cmap));
2668: #endif
2669: }
2670: PetscCall(PetscFree(submatj->row2proc));
2672: PetscCall(PetscFree(submatj));
2673: PetscFunctionReturn(PETSC_SUCCESS);
2674: }
2676: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2677: {
2678: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2679: Mat_SubSppt *submatj = c->submatis1;
2681: PetscFunctionBegin;
2682: PetscCall((*submatj->destroy)(C));
2683: PetscCall(MatDestroySubMatrix_Private(submatj));
2684: PetscFunctionReturn(PETSC_SUCCESS);
2685: }
2687: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2688: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2689: {
2690: PetscInt i;
2691: Mat C;
2692: Mat_SeqAIJ *c;
2693: Mat_SubSppt *submatj;
2695: PetscFunctionBegin;
2696: for (i = 0; i < n; i++) {
2697: C = (*mat)[i];
2698: c = (Mat_SeqAIJ *)C->data;
2699: submatj = c->submatis1;
2700: if (submatj) {
2701: if (--((PetscObject)C)->refct <= 0) {
2702: PetscCall(PetscFree(C->factorprefix));
2703: PetscCall((*submatj->destroy)(C));
2704: PetscCall(MatDestroySubMatrix_Private(submatj));
2705: PetscCall(PetscFree(C->defaultvectype));
2706: PetscCall(PetscFree(C->defaultrandtype));
2707: PetscCall(PetscLayoutDestroy(&C->rmap));
2708: PetscCall(PetscLayoutDestroy(&C->cmap));
2709: PetscCall(PetscHeaderDestroy(&C));
2710: }
2711: } else {
2712: PetscCall(MatDestroy(&C));
2713: }
2714: }
2716: /* Destroy Dummy submatrices created for reuse */
2717: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2719: PetscCall(PetscFree(*mat));
2720: PetscFunctionReturn(PETSC_SUCCESS);
2721: }
2723: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2724: {
2725: PetscInt i;
2727: PetscFunctionBegin;
2728: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2730: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2731: PetscFunctionReturn(PETSC_SUCCESS);
2732: }
2734: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2735: {
2736: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2737: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2738: const PetscInt *idx;
2739: PetscInt start, end, *ai, *aj, bs = A->rmap->bs == A->cmap->bs ? A->rmap->bs : 1;
2740: PetscBT table;
2742: PetscFunctionBegin;
2743: m = A->rmap->n / bs;
2744: ai = a->i;
2745: aj = a->j;
2747: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2749: PetscCall(PetscMalloc1(m + 1, &nidx));
2750: PetscCall(PetscBTCreate(m, &table));
2752: for (i = 0; i < is_max; i++) {
2753: /* Initialize the two local arrays */
2754: isz = 0;
2755: PetscCall(PetscBTMemzero(m, table));
2757: /* Extract the indices, assume there can be duplicate entries */
2758: PetscCall(ISGetIndices(is[i], &idx));
2759: PetscCall(ISGetLocalSize(is[i], &n));
2761: if (bs > 1) {
2762: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2763: for (j = 0; j < n; ++j) {
2764: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2765: }
2766: PetscCall(ISRestoreIndices(is[i], &idx));
2767: PetscCall(ISDestroy(&is[i]));
2769: k = 0;
2770: for (j = 0; j < ov; j++) { /* for each overlap */
2771: n = isz;
2772: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2773: for (ll = 0; ll < bs; ll++) {
2774: row = bs * nidx[k] + ll;
2775: start = ai[row];
2776: end = ai[row + 1];
2777: for (l = start; l < end; l++) {
2778: val = aj[l] / bs;
2779: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2780: }
2781: }
2782: }
2783: }
2784: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, is + i));
2785: } else {
2786: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2787: for (j = 0; j < n; ++j) {
2788: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2789: }
2790: PetscCall(ISRestoreIndices(is[i], &idx));
2791: PetscCall(ISDestroy(&is[i]));
2793: k = 0;
2794: for (j = 0; j < ov; j++) { /* for each overlap */
2795: n = isz;
2796: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2797: row = nidx[k];
2798: start = ai[row];
2799: end = ai[row + 1];
2800: for (l = start; l < end; l++) {
2801: val = aj[l];
2802: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2803: }
2804: }
2805: }
2806: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, is + i));
2807: }
2808: }
2809: PetscCall(PetscBTDestroy(&table));
2810: PetscCall(PetscFree(nidx));
2811: PetscFunctionReturn(PETSC_SUCCESS);
2812: }
2814: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2815: {
2816: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2817: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2818: const PetscInt *row, *col;
2819: PetscInt *cnew, j, *lens;
2820: IS icolp, irowp;
2821: PetscInt *cwork = NULL;
2822: PetscScalar *vwork = NULL;
2824: PetscFunctionBegin;
2825: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2826: PetscCall(ISGetIndices(irowp, &row));
2827: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2828: PetscCall(ISGetIndices(icolp, &col));
2830: /* determine lengths of permuted rows */
2831: PetscCall(PetscMalloc1(m + 1, &lens));
2832: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2833: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2834: PetscCall(MatSetSizes(*B, m, n, m, n));
2835: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2836: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2837: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2838: PetscCall(PetscFree(lens));
2840: PetscCall(PetscMalloc1(n, &cnew));
2841: for (i = 0; i < m; i++) {
2842: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2843: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2844: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2845: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2846: }
2847: PetscCall(PetscFree(cnew));
2849: (*B)->assembled = PETSC_FALSE;
2851: #if defined(PETSC_HAVE_DEVICE)
2852: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2853: #endif
2854: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2855: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2856: PetscCall(ISRestoreIndices(irowp, &row));
2857: PetscCall(ISRestoreIndices(icolp, &col));
2858: PetscCall(ISDestroy(&irowp));
2859: PetscCall(ISDestroy(&icolp));
2860: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2861: PetscFunctionReturn(PETSC_SUCCESS);
2862: }
2864: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2865: {
2866: PetscFunctionBegin;
2867: /* If the two matrices have the same copy implementation, use fast copy. */
2868: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2869: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2870: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2871: const PetscScalar *aa;
2872: PetscScalar *bb;
2874: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2875: PetscCall(MatSeqAIJGetArrayWrite(B, &bb));
2877: 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]);
2878: PetscCall(PetscArraycpy(bb, aa, a->i[A->rmap->n]));
2879: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2880: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2881: PetscCall(MatSeqAIJRestoreArrayWrite(B, &bb));
2882: } else {
2883: PetscCall(MatCopy_Basic(A, B, str));
2884: }
2885: PetscFunctionReturn(PETSC_SUCCESS);
2886: }
2888: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2889: {
2890: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2892: PetscFunctionBegin;
2893: *array = a->a;
2894: PetscFunctionReturn(PETSC_SUCCESS);
2895: }
2897: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2898: {
2899: PetscFunctionBegin;
2900: *array = NULL;
2901: PetscFunctionReturn(PETSC_SUCCESS);
2902: }
2904: /*
2905: Computes the number of nonzeros per row needed for preallocation when X and Y
2906: have different nonzero structure.
2907: */
2908: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2909: {
2910: PetscInt i, j, k, nzx, nzy;
2912: PetscFunctionBegin;
2913: /* Set the number of nonzeros in the new matrix */
2914: for (i = 0; i < m; i++) {
2915: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2916: nzx = xi[i + 1] - xi[i];
2917: nzy = yi[i + 1] - yi[i];
2918: nnz[i] = 0;
2919: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2920: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
2921: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
2922: nnz[i]++;
2923: }
2924: for (; k < nzy; k++) nnz[i]++;
2925: }
2926: PetscFunctionReturn(PETSC_SUCCESS);
2927: }
2929: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
2930: {
2931: PetscInt m = Y->rmap->N;
2932: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2933: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2935: PetscFunctionBegin;
2936: /* Set the number of nonzeros in the new matrix */
2937: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
2938: PetscFunctionReturn(PETSC_SUCCESS);
2939: }
2941: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2942: {
2943: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
2945: PetscFunctionBegin;
2946: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
2947: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
2948: if (e) {
2949: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
2950: if (e) {
2951: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
2952: if (e) str = SAME_NONZERO_PATTERN;
2953: }
2954: }
2955: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
2956: }
2957: if (str == SAME_NONZERO_PATTERN) {
2958: const PetscScalar *xa;
2959: PetscScalar *ya, alpha = a;
2960: PetscBLASInt one = 1, bnz;
2962: PetscCall(PetscBLASIntCast(x->nz, &bnz));
2963: PetscCall(MatSeqAIJGetArray(Y, &ya));
2964: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
2965: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
2966: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
2967: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
2968: PetscCall(PetscLogFlops(2.0 * bnz));
2969: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
2970: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2971: PetscCall(MatAXPY_Basic(Y, a, X, str));
2972: } else {
2973: Mat B;
2974: PetscInt *nnz;
2975: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
2976: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2977: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2978: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2979: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2980: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
2981: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
2982: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2983: PetscCall(MatHeaderMerge(Y, &B));
2984: PetscCall(MatSeqAIJCheckInode(Y));
2985: PetscCall(PetscFree(nnz));
2986: }
2987: PetscFunctionReturn(PETSC_SUCCESS);
2988: }
2990: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
2991: {
2992: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2993: PetscInt i, nz = aij->nz;
2994: PetscScalar *a;
2996: PetscFunctionBegin;
2997: PetscCall(MatSeqAIJGetArray(mat, &a));
2998: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
2999: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3000: PetscFunctionReturn(PETSC_SUCCESS);
3001: }
3003: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3004: {
3005: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3006: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3007: PetscReal atmp;
3008: PetscScalar *x;
3009: const MatScalar *aa, *av;
3011: PetscFunctionBegin;
3012: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3013: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3014: aa = av;
3015: ai = a->i;
3016: aj = a->j;
3018: PetscCall(VecGetArrayWrite(v, &x));
3019: PetscCall(VecGetLocalSize(v, &n));
3020: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3021: for (i = 0; i < m; i++) {
3022: ncols = ai[1] - ai[0];
3023: ai++;
3024: x[i] = 0;
3025: for (j = 0; j < ncols; j++) {
3026: atmp = PetscAbsScalar(*aa);
3027: if (PetscAbsScalar(x[i]) < atmp) {
3028: x[i] = atmp;
3029: if (idx) idx[i] = *aj;
3030: }
3031: aa++;
3032: aj++;
3033: }
3034: }
3035: PetscCall(VecRestoreArrayWrite(v, &x));
3036: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3037: PetscFunctionReturn(PETSC_SUCCESS);
3038: }
3040: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3041: {
3042: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3043: PetscInt i, j, m = A->rmap->n, *ai, ncols, n;
3044: PetscScalar *x;
3045: const MatScalar *aa, *av;
3047: PetscFunctionBegin;
3048: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3049: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3050: aa = av;
3051: ai = a->i;
3053: PetscCall(VecGetArrayWrite(v, &x));
3054: PetscCall(VecGetLocalSize(v, &n));
3055: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3056: for (i = 0; i < m; i++) {
3057: ncols = ai[1] - ai[0];
3058: ai++;
3059: x[i] = 0;
3060: for (j = 0; j < ncols; j++) {
3061: x[i] += PetscAbsScalar(*aa);
3062: aa++;
3063: }
3064: }
3065: PetscCall(VecRestoreArrayWrite(v, &x));
3066: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3067: PetscFunctionReturn(PETSC_SUCCESS);
3068: }
3070: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3071: {
3072: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3073: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3074: PetscScalar *x;
3075: const MatScalar *aa, *av;
3077: PetscFunctionBegin;
3078: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3079: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3080: aa = av;
3081: ai = a->i;
3082: aj = a->j;
3084: PetscCall(VecGetArrayWrite(v, &x));
3085: PetscCall(VecGetLocalSize(v, &n));
3086: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3087: for (i = 0; i < m; i++) {
3088: ncols = ai[1] - ai[0];
3089: ai++;
3090: if (ncols == A->cmap->n) { /* row is dense */
3091: x[i] = *aa;
3092: if (idx) idx[i] = 0;
3093: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3094: x[i] = 0.0;
3095: if (idx) {
3096: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3097: if (aj[j] > j) {
3098: idx[i] = j;
3099: break;
3100: }
3101: }
3102: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3103: if (j == ncols && j < A->cmap->n) idx[i] = j;
3104: }
3105: }
3106: for (j = 0; j < ncols; j++) {
3107: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3108: x[i] = *aa;
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 MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3121: {
3122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3123: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3124: PetscScalar *x;
3125: const MatScalar *aa, *av;
3127: PetscFunctionBegin;
3128: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3129: aa = av;
3130: ai = a->i;
3131: aj = a->j;
3133: PetscCall(VecGetArrayWrite(v, &x));
3134: PetscCall(VecGetLocalSize(v, &n));
3135: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3136: for (i = 0; i < m; i++) {
3137: ncols = ai[1] - ai[0];
3138: ai++;
3139: if (ncols == A->cmap->n) { /* row is dense */
3140: x[i] = *aa;
3141: if (idx) idx[i] = 0;
3142: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3143: x[i] = 0.0;
3144: if (idx) { /* find first implicit 0.0 in the row */
3145: for (j = 0; j < ncols; j++) {
3146: if (aj[j] > j) {
3147: idx[i] = j;
3148: break;
3149: }
3150: }
3151: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3152: if (j == ncols && j < A->cmap->n) idx[i] = j;
3153: }
3154: }
3155: for (j = 0; j < ncols; j++) {
3156: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3157: x[i] = *aa;
3158: if (idx) idx[i] = *aj;
3159: }
3160: aa++;
3161: aj++;
3162: }
3163: }
3164: PetscCall(VecRestoreArrayWrite(v, &x));
3165: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3166: PetscFunctionReturn(PETSC_SUCCESS);
3167: }
3169: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3170: {
3171: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3172: PetscInt i, j, m = A->rmap->n, ncols, n;
3173: const PetscInt *ai, *aj;
3174: PetscScalar *x;
3175: const MatScalar *aa, *av;
3177: PetscFunctionBegin;
3178: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3179: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3180: aa = av;
3181: ai = a->i;
3182: aj = a->j;
3184: PetscCall(VecGetArrayWrite(v, &x));
3185: PetscCall(VecGetLocalSize(v, &n));
3186: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3187: for (i = 0; i < m; i++) {
3188: ncols = ai[1] - ai[0];
3189: ai++;
3190: if (ncols == A->cmap->n) { /* row is dense */
3191: x[i] = *aa;
3192: if (idx) idx[i] = 0;
3193: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3194: x[i] = 0.0;
3195: if (idx) { /* find first implicit 0.0 in the row */
3196: for (j = 0; j < ncols; j++) {
3197: if (aj[j] > j) {
3198: idx[i] = j;
3199: break;
3200: }
3201: }
3202: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3203: if (j == ncols && j < A->cmap->n) idx[i] = j;
3204: }
3205: }
3206: for (j = 0; j < ncols; j++) {
3207: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3208: x[i] = *aa;
3209: if (idx) idx[i] = *aj;
3210: }
3211: aa++;
3212: aj++;
3213: }
3214: }
3215: PetscCall(VecRestoreArrayWrite(v, &x));
3216: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3217: PetscFunctionReturn(PETSC_SUCCESS);
3218: }
3220: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3221: {
3222: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3223: PetscInt i, bs = A->rmap->bs, mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3224: MatScalar *diag, work[25], *v_work;
3225: const PetscReal shift = 0.0;
3226: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3228: PetscFunctionBegin;
3229: allowzeropivot = PetscNot(A->erroriffailure);
3230: if (a->ibdiagvalid) {
3231: if (values) *values = a->ibdiag;
3232: PetscFunctionReturn(PETSC_SUCCESS);
3233: }
3234: if (!a->ibdiag) PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag));
3235: diag = a->ibdiag;
3236: if (values) *values = a->ibdiag;
3237: /* factor and invert each block */
3238: switch (bs) {
3239: case 1:
3240: for (i = 0; i < mbs; i++) {
3241: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3242: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3243: PetscCheck(allowzeropivot, PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON);
3244: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3245: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3246: A->factorerror_zeropivot_row = i;
3247: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3248: }
3249: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3250: }
3251: break;
3252: case 2:
3253: for (i = 0; i < mbs; i++) {
3254: ij[0] = 2 * i;
3255: ij[1] = 2 * i + 1;
3256: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3257: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3258: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3259: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3260: diag += 4;
3261: }
3262: break;
3263: case 3:
3264: for (i = 0; i < mbs; i++) {
3265: ij[0] = 3 * i;
3266: ij[1] = 3 * i + 1;
3267: ij[2] = 3 * i + 2;
3268: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3269: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3270: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3271: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3272: diag += 9;
3273: }
3274: break;
3275: case 4:
3276: for (i = 0; i < mbs; i++) {
3277: ij[0] = 4 * i;
3278: ij[1] = 4 * i + 1;
3279: ij[2] = 4 * i + 2;
3280: ij[3] = 4 * i + 3;
3281: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3282: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3283: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3284: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3285: diag += 16;
3286: }
3287: break;
3288: case 5:
3289: for (i = 0; i < mbs; i++) {
3290: ij[0] = 5 * i;
3291: ij[1] = 5 * i + 1;
3292: ij[2] = 5 * i + 2;
3293: ij[3] = 5 * i + 3;
3294: ij[4] = 5 * i + 4;
3295: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3296: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3297: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3298: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3299: diag += 25;
3300: }
3301: break;
3302: case 6:
3303: for (i = 0; i < mbs; i++) {
3304: ij[0] = 6 * i;
3305: ij[1] = 6 * i + 1;
3306: ij[2] = 6 * i + 2;
3307: ij[3] = 6 * i + 3;
3308: ij[4] = 6 * i + 4;
3309: ij[5] = 6 * i + 5;
3310: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3311: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3312: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3313: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3314: diag += 36;
3315: }
3316: break;
3317: case 7:
3318: for (i = 0; i < mbs; i++) {
3319: ij[0] = 7 * i;
3320: ij[1] = 7 * i + 1;
3321: ij[2] = 7 * i + 2;
3322: ij[3] = 7 * i + 3;
3323: ij[4] = 7 * i + 4;
3324: ij[5] = 7 * i + 5;
3325: ij[6] = 7 * i + 6;
3326: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3327: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3328: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3329: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3330: diag += 49;
3331: }
3332: break;
3333: default:
3334: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3335: for (i = 0; i < mbs; i++) {
3336: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3337: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3338: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3339: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3340: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3341: diag += bs2;
3342: }
3343: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3344: }
3345: a->ibdiagvalid = PETSC_TRUE;
3346: PetscFunctionReturn(PETSC_SUCCESS);
3347: }
3349: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3350: {
3351: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3352: PetscScalar a, *aa;
3353: PetscInt m, n, i, j, col;
3355: PetscFunctionBegin;
3356: if (!x->assembled) {
3357: PetscCall(MatGetSize(x, &m, &n));
3358: for (i = 0; i < m; i++) {
3359: for (j = 0; j < aij->imax[i]; j++) {
3360: PetscCall(PetscRandomGetValue(rctx, &a));
3361: col = (PetscInt)(n * PetscRealPart(a));
3362: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3363: }
3364: }
3365: } else {
3366: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3367: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3368: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3369: }
3370: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3371: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3372: PetscFunctionReturn(PETSC_SUCCESS);
3373: }
3375: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3376: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3377: {
3378: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3379: PetscScalar a;
3380: PetscInt m, n, i, j, col, nskip;
3382: PetscFunctionBegin;
3383: nskip = high - low;
3384: PetscCall(MatGetSize(x, &m, &n));
3385: n -= nskip; /* shrink number of columns where nonzeros can be set */
3386: for (i = 0; i < m; i++) {
3387: for (j = 0; j < aij->imax[i]; j++) {
3388: PetscCall(PetscRandomGetValue(rctx, &a));
3389: col = (PetscInt)(n * PetscRealPart(a));
3390: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3391: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3392: }
3393: }
3394: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3395: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3396: PetscFunctionReturn(PETSC_SUCCESS);
3397: }
3399: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3400: MatGetRow_SeqAIJ,
3401: MatRestoreRow_SeqAIJ,
3402: MatMult_SeqAIJ,
3403: /* 4*/ MatMultAdd_SeqAIJ,
3404: MatMultTranspose_SeqAIJ,
3405: MatMultTransposeAdd_SeqAIJ,
3406: NULL,
3407: NULL,
3408: NULL,
3409: /* 10*/ NULL,
3410: MatLUFactor_SeqAIJ,
3411: NULL,
3412: MatSOR_SeqAIJ,
3413: MatTranspose_SeqAIJ,
3414: /* 15*/ MatGetInfo_SeqAIJ,
3415: MatEqual_SeqAIJ,
3416: MatGetDiagonal_SeqAIJ,
3417: MatDiagonalScale_SeqAIJ,
3418: MatNorm_SeqAIJ,
3419: /* 20*/ NULL,
3420: MatAssemblyEnd_SeqAIJ,
3421: MatSetOption_SeqAIJ,
3422: MatZeroEntries_SeqAIJ,
3423: /* 24*/ MatZeroRows_SeqAIJ,
3424: NULL,
3425: NULL,
3426: NULL,
3427: NULL,
3428: /* 29*/ MatSetUp_Seq_Hash,
3429: NULL,
3430: NULL,
3431: NULL,
3432: NULL,
3433: /* 34*/ MatDuplicate_SeqAIJ,
3434: NULL,
3435: NULL,
3436: MatILUFactor_SeqAIJ,
3437: NULL,
3438: /* 39*/ MatAXPY_SeqAIJ,
3439: MatCreateSubMatrices_SeqAIJ,
3440: MatIncreaseOverlap_SeqAIJ,
3441: MatGetValues_SeqAIJ,
3442: MatCopy_SeqAIJ,
3443: /* 44*/ MatGetRowMax_SeqAIJ,
3444: MatScale_SeqAIJ,
3445: MatShift_SeqAIJ,
3446: MatDiagonalSet_SeqAIJ,
3447: MatZeroRowsColumns_SeqAIJ,
3448: /* 49*/ MatSetRandom_SeqAIJ,
3449: MatGetRowIJ_SeqAIJ,
3450: MatRestoreRowIJ_SeqAIJ,
3451: MatGetColumnIJ_SeqAIJ,
3452: MatRestoreColumnIJ_SeqAIJ,
3453: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3454: NULL,
3455: NULL,
3456: MatPermute_SeqAIJ,
3457: NULL,
3458: /* 59*/ NULL,
3459: MatDestroy_SeqAIJ,
3460: MatView_SeqAIJ,
3461: NULL,
3462: NULL,
3463: /* 64*/ MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3464: NULL,
3465: NULL,
3466: NULL,
3467: MatGetRowMaxAbs_SeqAIJ,
3468: /* 69*/ MatGetRowMinAbs_SeqAIJ,
3469: NULL,
3470: NULL,
3471: MatFDColoringApply_AIJ,
3472: NULL,
3473: /* 74*/ MatFindZeroDiagonals_SeqAIJ,
3474: NULL,
3475: NULL,
3476: NULL,
3477: MatLoad_SeqAIJ,
3478: /* 79*/ NULL,
3479: NULL,
3480: NULL,
3481: NULL,
3482: NULL,
3483: /* 84*/ NULL,
3484: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3485: MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3486: NULL,
3487: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3488: /* 90*/ NULL,
3489: MatProductSetFromOptions_SeqAIJ,
3490: NULL,
3491: NULL,
3492: MatConjugate_SeqAIJ,
3493: /* 94*/ NULL,
3494: MatSetValuesRow_SeqAIJ,
3495: MatRealPart_SeqAIJ,
3496: MatImaginaryPart_SeqAIJ,
3497: NULL,
3498: /* 99*/ NULL,
3499: MatMatSolve_SeqAIJ,
3500: NULL,
3501: MatGetRowMin_SeqAIJ,
3502: NULL,
3503: /*104*/ NULL,
3504: NULL,
3505: NULL,
3506: NULL,
3507: NULL,
3508: /*109*/ NULL,
3509: NULL,
3510: NULL,
3511: NULL,
3512: MatGetMultiProcBlock_SeqAIJ,
3513: /*114*/ MatFindNonzeroRows_SeqAIJ,
3514: MatGetColumnReductions_SeqAIJ,
3515: MatInvertBlockDiagonal_SeqAIJ,
3516: MatInvertVariableBlockDiagonal_SeqAIJ,
3517: NULL,
3518: /*119*/ NULL,
3519: NULL,
3520: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3521: MatTransposeColoringCreate_SeqAIJ,
3522: MatTransColoringApplySpToDen_SeqAIJ,
3523: /*124*/ MatTransColoringApplyDenToSp_SeqAIJ,
3524: MatRARtNumeric_SeqAIJ_SeqAIJ,
3525: NULL,
3526: NULL,
3527: MatFDColoringSetUp_SeqXAIJ,
3528: /*129*/ MatFindOffBlockDiagonalEntries_SeqAIJ,
3529: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3530: MatDestroySubMatrices_SeqAIJ,
3531: NULL,
3532: NULL,
3533: /*134*/ MatCreateGraph_Simple_AIJ,
3534: MatTransposeSymbolic_SeqAIJ,
3535: MatEliminateZeros_SeqAIJ,
3536: MatGetRowSumAbs_SeqAIJ,
3537: NULL,
3538: /*139*/ NULL,
3539: NULL,
3540: MatCopyHashToXAIJ_Seq_Hash,
3541: NULL,
3542: NULL};
3544: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3545: {
3546: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3547: PetscInt i, nz, n;
3549: PetscFunctionBegin;
3550: nz = aij->maxnz;
3551: n = mat->rmap->n;
3552: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3553: aij->nz = nz;
3554: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3555: PetscFunctionReturn(PETSC_SUCCESS);
3556: }
3558: /*
3559: * Given a sparse matrix with global column indices, compact it by using a local column space.
3560: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3561: */
3562: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3563: {
3564: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3565: PetscHMapI gid1_lid1;
3566: PetscHashIter tpos;
3567: PetscInt gid, lid, i, ec, nz = aij->nz;
3568: PetscInt *garray, *jj = aij->j;
3570: PetscFunctionBegin;
3572: PetscAssertPointer(mapping, 2);
3573: /* use a table */
3574: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3575: ec = 0;
3576: for (i = 0; i < nz; i++) {
3577: PetscInt data, gid1 = jj[i] + 1;
3578: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3579: if (!data) {
3580: /* one based table */
3581: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3582: }
3583: }
3584: /* form array of columns we need */
3585: PetscCall(PetscMalloc1(ec, &garray));
3586: PetscHashIterBegin(gid1_lid1, tpos);
3587: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3588: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3589: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3590: PetscHashIterNext(gid1_lid1, tpos);
3591: gid--;
3592: lid--;
3593: garray[lid] = gid;
3594: }
3595: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3596: PetscCall(PetscHMapIClear(gid1_lid1));
3597: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3598: /* compact out the extra columns in B */
3599: for (i = 0; i < nz; i++) {
3600: PetscInt gid1 = jj[i] + 1;
3601: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3602: lid--;
3603: jj[i] = lid;
3604: }
3605: PetscCall(PetscLayoutDestroy(&mat->cmap));
3606: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3607: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3608: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3609: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3610: PetscFunctionReturn(PETSC_SUCCESS);
3611: }
3613: /*@
3614: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3615: in the matrix.
3617: Input Parameters:
3618: + mat - the `MATSEQAIJ` matrix
3619: - indices - the column indices
3621: Level: advanced
3623: Notes:
3624: This can be called if you have precomputed the nonzero structure of the
3625: matrix and want to provide it to the matrix object to improve the performance
3626: of the `MatSetValues()` operation.
3628: You MUST have set the correct numbers of nonzeros per row in the call to
3629: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3631: MUST be called before any calls to `MatSetValues()`
3633: The indices should start with zero, not one.
3635: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3636: @*/
3637: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3638: {
3639: PetscFunctionBegin;
3641: PetscAssertPointer(indices, 2);
3642: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3643: PetscFunctionReturn(PETSC_SUCCESS);
3644: }
3646: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3647: {
3648: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3649: size_t nz = aij->i[mat->rmap->n];
3651: PetscFunctionBegin;
3652: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3654: /* allocate space for values if not already there */
3655: if (!aij->saved_values) PetscCall(PetscMalloc1(nz + 1, &aij->saved_values));
3657: /* copy values over */
3658: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3659: PetscFunctionReturn(PETSC_SUCCESS);
3660: }
3662: /*@
3663: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3664: nonlinear portion.
3666: Logically Collect
3668: Input Parameter:
3669: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3671: Level: advanced
3673: Example Usage:
3674: .vb
3675: Using SNES
3676: Create Jacobian matrix
3677: Set linear terms into matrix
3678: Apply boundary conditions to matrix, at this time matrix must have
3679: final nonzero structure (i.e. setting the nonlinear terms and applying
3680: boundary conditions again will not change the nonzero structure
3681: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3682: MatStoreValues(mat);
3683: Call SNESSetJacobian() with matrix
3684: In your Jacobian routine
3685: MatRetrieveValues(mat);
3686: Set nonlinear terms in matrix
3688: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3689: // build linear portion of Jacobian
3690: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3691: MatStoreValues(mat);
3692: loop over nonlinear iterations
3693: MatRetrieveValues(mat);
3694: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3695: // call MatAssemblyBegin/End() on matrix
3696: Solve linear system with Jacobian
3697: endloop
3698: .ve
3700: Notes:
3701: Matrix must already be assembled before calling this routine
3702: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3703: calling this routine.
3705: When this is called multiple times it overwrites the previous set of stored values
3706: and does not allocated additional space.
3708: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3709: @*/
3710: PetscErrorCode MatStoreValues(Mat mat)
3711: {
3712: PetscFunctionBegin;
3714: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3715: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3716: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3717: PetscFunctionReturn(PETSC_SUCCESS);
3718: }
3720: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3721: {
3722: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3723: PetscInt nz = aij->i[mat->rmap->n];
3725: PetscFunctionBegin;
3726: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3727: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3728: /* copy values over */
3729: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3730: PetscFunctionReturn(PETSC_SUCCESS);
3731: }
3733: /*@
3734: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3736: Logically Collect
3738: Input Parameter:
3739: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3741: Level: advanced
3743: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3744: @*/
3745: PetscErrorCode MatRetrieveValues(Mat mat)
3746: {
3747: PetscFunctionBegin;
3749: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3750: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3751: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3752: PetscFunctionReturn(PETSC_SUCCESS);
3753: }
3755: /*@
3756: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3757: (the default parallel PETSc format). For good matrix assembly performance
3758: the user should preallocate the matrix storage by setting the parameter `nz`
3759: (or the array `nnz`).
3761: Collective
3763: Input Parameters:
3764: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3765: . m - number of rows
3766: . n - number of columns
3767: . nz - number of nonzeros per row (same for all rows)
3768: - nnz - array containing the number of nonzeros in the various rows
3769: (possibly different for each row) or NULL
3771: Output Parameter:
3772: . A - the matrix
3774: Options Database Keys:
3775: + -mat_no_inode - Do not use inodes
3776: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3778: Level: intermediate
3780: Notes:
3781: It is recommend to use `MatCreateFromOptions()` instead of this routine
3783: If `nnz` is given then `nz` is ignored
3785: The `MATSEQAIJ` format, also called
3786: compressed row storage, is fully compatible with standard Fortran
3787: storage. That is, the stored row and column indices can begin at
3788: either one (as in Fortran) or zero.
3790: Specify the preallocated storage with either `nz` or `nnz` (not both).
3791: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3792: allocation.
3794: By default, this format uses inodes (identical nodes) when possible, to
3795: improve numerical efficiency of matrix-vector products and solves. We
3796: search for consecutive rows with the same nonzero structure, thereby
3797: reusing matrix information to achieve increased efficiency.
3799: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3800: @*/
3801: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3802: {
3803: PetscFunctionBegin;
3804: PetscCall(MatCreate(comm, A));
3805: PetscCall(MatSetSizes(*A, m, n, m, n));
3806: PetscCall(MatSetType(*A, MATSEQAIJ));
3807: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3808: PetscFunctionReturn(PETSC_SUCCESS);
3809: }
3811: /*@
3812: MatSeqAIJSetPreallocation - For good matrix assembly performance
3813: the user should preallocate the matrix storage by setting the parameter nz
3814: (or the array nnz). By setting these parameters accurately, performance
3815: during matrix assembly can be increased by more than a factor of 50.
3817: Collective
3819: Input Parameters:
3820: + B - The matrix
3821: . nz - number of nonzeros per row (same for all rows)
3822: - nnz - array containing the number of nonzeros in the various rows
3823: (possibly different for each row) or NULL
3825: Options Database Keys:
3826: + -mat_no_inode - Do not use inodes
3827: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3829: Level: intermediate
3831: Notes:
3832: If `nnz` is given then `nz` is ignored
3834: The `MATSEQAIJ` format also called
3835: compressed row storage, is fully compatible with standard Fortran
3836: storage. That is, the stored row and column indices can begin at
3837: either one (as in Fortran) or zero. See the users' manual for details.
3839: Specify the preallocated storage with either `nz` or `nnz` (not both).
3840: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3841: allocation.
3843: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3844: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3845: You can also run with the option -info and look for messages with the string
3846: malloc in them to see if additional memory allocation was needed.
3848: Developer Notes:
3849: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3850: entries or columns indices
3852: By default, this format uses inodes (identical nodes) when possible, to
3853: improve numerical efficiency of matrix-vector products and solves. We
3854: search for consecutive rows with the same nonzero structure, thereby
3855: reusing matrix information to achieve increased efficiency.
3857: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3858: `MatSeqAIJSetTotalPreallocation()`
3859: @*/
3860: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3861: {
3862: PetscFunctionBegin;
3865: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3866: PetscFunctionReturn(PETSC_SUCCESS);
3867: }
3869: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3870: {
3871: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3872: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3873: PetscInt i;
3875: PetscFunctionBegin;
3876: if (B->hash_active) {
3877: B->ops[0] = b->cops;
3878: PetscCall(PetscHMapIJVDestroy(&b->ht));
3879: PetscCall(PetscFree(b->dnz));
3880: B->hash_active = PETSC_FALSE;
3881: }
3882: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3883: if (nz == MAT_SKIP_ALLOCATION) {
3884: skipallocation = PETSC_TRUE;
3885: nz = 0;
3886: }
3887: PetscCall(PetscLayoutSetUp(B->rmap));
3888: PetscCall(PetscLayoutSetUp(B->cmap));
3890: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3891: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3892: if (nnz) {
3893: for (i = 0; i < B->rmap->n; i++) {
3894: 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]);
3895: 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);
3896: }
3897: }
3899: B->preallocated = PETSC_TRUE;
3900: if (!skipallocation) {
3901: if (!b->imax) PetscCall(PetscMalloc1(B->rmap->n, &b->imax));
3902: if (!b->ilen) {
3903: /* b->ilen will count nonzeros in each row so far. */
3904: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
3905: } else {
3906: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
3907: }
3908: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
3909: if (!nnz) {
3910: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3911: else if (nz < 0) nz = 1;
3912: nz = PetscMin(nz, B->cmap->n);
3913: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
3914: PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
3915: } else {
3916: PetscInt64 nz64 = 0;
3917: for (i = 0; i < B->rmap->n; i++) {
3918: b->imax[i] = nnz[i];
3919: nz64 += nnz[i];
3920: }
3921: PetscCall(PetscIntCast(nz64, &nz));
3922: }
3924: /* allocate the matrix space */
3925: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
3926: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
3927: PetscCall(PetscShmgetAllocateArray(B->rmap->n + 1, sizeof(PetscInt), (void **)&b->i));
3928: b->free_ij = PETSC_TRUE;
3929: if (B->structure_only) {
3930: b->free_a = PETSC_FALSE;
3931: } else {
3932: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)&b->a));
3933: b->free_a = PETSC_TRUE;
3934: }
3935: b->i[0] = 0;
3936: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
3937: } else {
3938: b->free_a = PETSC_FALSE;
3939: b->free_ij = PETSC_FALSE;
3940: }
3942: if (b->ipre && nnz != b->ipre && b->imax) {
3943: /* reserve user-requested sparsity */
3944: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
3945: }
3947: b->nz = 0;
3948: b->maxnz = nz;
3949: B->info.nz_unneeded = (double)b->maxnz;
3950: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
3951: B->was_assembled = PETSC_FALSE;
3952: B->assembled = PETSC_FALSE;
3953: /* We simply deem preallocation has changed nonzero state. Updating the state
3954: will give clients (like AIJKokkos) a chance to know something has happened.
3955: */
3956: B->nonzerostate++;
3957: PetscFunctionReturn(PETSC_SUCCESS);
3958: }
3960: PetscErrorCode MatResetPreallocation_SeqAIJ_Private(Mat A, PetscBool *memoryreset)
3961: {
3962: Mat_SeqAIJ *a;
3963: PetscInt i;
3964: PetscBool skipreset;
3966: PetscFunctionBegin;
3969: 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()");
3970: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
3972: /* Check local size. If zero, then return */
3973: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
3975: a = (Mat_SeqAIJ *)A->data;
3976: /* if no saved info, we error out */
3977: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
3979: PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and cannot reset preallocation ");
3981: PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
3982: if (skipreset) PetscCall(MatZeroEntries(A));
3983: else {
3984: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
3985: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
3986: a->i[0] = 0;
3987: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
3988: A->preallocated = PETSC_TRUE;
3989: a->nz = 0;
3990: a->maxnz = a->i[A->rmap->n];
3991: A->info.nz_unneeded = (double)a->maxnz;
3992: A->was_assembled = PETSC_FALSE;
3993: A->assembled = PETSC_FALSE;
3994: A->nonzerostate++;
3995: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
3996: PetscCall(PetscObjectStateIncrease((PetscObject)A));
3997: }
3998: if (memoryreset) *memoryreset = (PetscBool)!skipreset;
3999: PetscFunctionReturn(PETSC_SUCCESS);
4000: }
4002: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4003: {
4004: PetscFunctionBegin;
4005: PetscCall(MatResetPreallocation_SeqAIJ_Private(A, NULL));
4006: PetscFunctionReturn(PETSC_SUCCESS);
4007: }
4009: /*@
4010: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4012: Input Parameters:
4013: + B - the matrix
4014: . i - the indices into `j` for the start of each row (indices start with zero)
4015: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4016: - v - optional values in the matrix, use `NULL` if not provided
4018: Level: developer
4020: Notes:
4021: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4023: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4024: structure will be the union of all the previous nonzero structures.
4026: Developer Notes:
4027: 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
4028: then just copies the `v` values directly with `PetscMemcpy()`.
4030: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4032: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4033: @*/
4034: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4035: {
4036: PetscFunctionBegin;
4039: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4040: PetscFunctionReturn(PETSC_SUCCESS);
4041: }
4043: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4044: {
4045: PetscInt i;
4046: PetscInt m, n;
4047: PetscInt nz;
4048: PetscInt *nnz;
4050: PetscFunctionBegin;
4051: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4053: PetscCall(PetscLayoutSetUp(B->rmap));
4054: PetscCall(PetscLayoutSetUp(B->cmap));
4056: PetscCall(MatGetSize(B, &m, &n));
4057: PetscCall(PetscMalloc1(m + 1, &nnz));
4058: for (i = 0; i < m; i++) {
4059: nz = Ii[i + 1] - Ii[i];
4060: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4061: nnz[i] = nz;
4062: }
4063: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4064: PetscCall(PetscFree(nnz));
4066: 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));
4068: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4069: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4071: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4072: PetscFunctionReturn(PETSC_SUCCESS);
4073: }
4075: /*@
4076: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4078: Input Parameters:
4079: + A - left-hand side matrix
4080: . B - right-hand side matrix
4081: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4083: Output Parameter:
4084: . C - Kronecker product of `A` and `B`
4086: Level: intermediate
4088: Note:
4089: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4091: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4092: @*/
4093: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4094: {
4095: PetscFunctionBegin;
4100: PetscAssertPointer(C, 4);
4101: if (reuse == MAT_REUSE_MATRIX) {
4104: }
4105: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4106: PetscFunctionReturn(PETSC_SUCCESS);
4107: }
4109: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4110: {
4111: Mat newmat;
4112: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4113: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4114: PetscScalar *v;
4115: const PetscScalar *aa, *ba;
4116: 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;
4117: PetscBool flg;
4119: PetscFunctionBegin;
4120: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4121: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4122: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4123: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4124: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4125: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4126: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4127: if (reuse == MAT_INITIAL_MATRIX) {
4128: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4129: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4130: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4131: PetscCall(MatSetType(newmat, MATAIJ));
4132: i[0] = 0;
4133: for (m = 0; m < am; ++m) {
4134: for (p = 0; p < bm; ++p) {
4135: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4136: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4137: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4138: }
4139: }
4140: }
4141: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4142: *C = newmat;
4143: PetscCall(PetscFree2(i, j));
4144: nnz = 0;
4145: }
4146: PetscCall(MatSeqAIJGetArray(*C, &v));
4147: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4148: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4149: for (m = 0; m < am; ++m) {
4150: for (p = 0; p < bm; ++p) {
4151: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4152: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4153: }
4154: }
4155: }
4156: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4157: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4158: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4159: PetscFunctionReturn(PETSC_SUCCESS);
4160: }
4162: #include <../src/mat/impls/dense/seq/dense.h>
4163: #include <petsc/private/kernels/petscaxpy.h>
4165: /*
4166: Computes (B'*A')' since computing B*A directly is untenable
4168: n p p
4169: [ ] [ ] [ ]
4170: m [ A ] * n [ B ] = m [ C ]
4171: [ ] [ ] [ ]
4173: */
4174: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4175: {
4176: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4177: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4178: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4179: PetscInt i, j, n, m, q, p;
4180: const PetscInt *ii, *idx;
4181: const PetscScalar *b, *a, *a_q;
4182: PetscScalar *c, *c_q;
4183: PetscInt clda = sub_c->lda;
4184: PetscInt alda = sub_a->lda;
4186: PetscFunctionBegin;
4187: m = A->rmap->n;
4188: n = A->cmap->n;
4189: p = B->cmap->n;
4190: a = sub_a->v;
4191: b = sub_b->a;
4192: c = sub_c->v;
4193: if (clda == m) {
4194: PetscCall(PetscArrayzero(c, m * p));
4195: } else {
4196: for (j = 0; j < p; j++)
4197: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4198: }
4199: ii = sub_b->i;
4200: idx = sub_b->j;
4201: for (i = 0; i < n; i++) {
4202: q = ii[i + 1] - ii[i];
4203: while (q-- > 0) {
4204: c_q = c + clda * (*idx);
4205: a_q = a + alda * i;
4206: PetscKernelAXPY(c_q, *b, a_q, m);
4207: idx++;
4208: b++;
4209: }
4210: }
4211: PetscFunctionReturn(PETSC_SUCCESS);
4212: }
4214: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4215: {
4216: PetscInt m = A->rmap->n, n = B->cmap->n;
4217: PetscBool cisdense;
4219: PetscFunctionBegin;
4220: 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);
4221: PetscCall(MatSetSizes(C, m, n, m, n));
4222: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4223: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4224: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4225: PetscCall(MatSetUp(C));
4227: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4228: PetscFunctionReturn(PETSC_SUCCESS);
4229: }
4231: /*MC
4232: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4233: based on compressed sparse row format.
4235: Options Database Key:
4236: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4238: Level: beginner
4240: Notes:
4241: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4242: in this case the values associated with the rows and columns one passes in are set to zero
4243: in the matrix
4245: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4246: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4248: Developer Note:
4249: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4251: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4252: M*/
4254: /*MC
4255: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4257: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4258: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4259: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4260: for communicators controlling multiple processes. It is recommended that you call both of
4261: the above preallocation routines for simplicity.
4263: Options Database Key:
4264: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4266: Level: beginner
4268: Note:
4269: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4270: enough exist.
4272: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4273: M*/
4275: /*MC
4276: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4278: Options Database Key:
4279: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4281: Level: beginner
4283: Note:
4284: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4285: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4286: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4287: for communicators controlling multiple processes. It is recommended that you call both of
4288: the above preallocation routines for simplicity.
4290: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4291: M*/
4293: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4294: #if defined(PETSC_HAVE_ELEMENTAL)
4295: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4296: #endif
4297: #if defined(PETSC_HAVE_SCALAPACK) && (defined(PETSC_USE_REAL_SINGLE) || defined(PETSC_USE_REAL_DOUBLE))
4298: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4299: #endif
4300: #if defined(PETSC_HAVE_HYPRE)
4301: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4302: #endif
4304: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4305: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4306: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4308: /*@C
4309: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4311: Not Collective
4313: Input Parameter:
4314: . A - a `MATSEQAIJ` matrix
4316: Output Parameter:
4317: . array - pointer to the data
4319: Level: intermediate
4321: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`
4322: @*/
4323: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar *array[])
4324: {
4325: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4327: PetscFunctionBegin;
4328: if (aij->ops->getarray) {
4329: PetscCall((*aij->ops->getarray)(A, array));
4330: } else {
4331: *array = aij->a;
4332: }
4333: PetscFunctionReturn(PETSC_SUCCESS);
4334: }
4336: /*@C
4337: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4339: Not Collective
4341: Input Parameters:
4342: + A - a `MATSEQAIJ` matrix
4343: - array - pointer to the data
4345: Level: intermediate
4347: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`
4348: @*/
4349: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar *array[])
4350: {
4351: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4353: PetscFunctionBegin;
4354: if (aij->ops->restorearray) {
4355: PetscCall((*aij->ops->restorearray)(A, array));
4356: } else {
4357: *array = NULL;
4358: }
4359: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4360: PetscFunctionReturn(PETSC_SUCCESS);
4361: }
4363: /*@C
4364: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4366: Not Collective; No Fortran Support
4368: Input Parameter:
4369: . A - a `MATSEQAIJ` matrix
4371: Output Parameter:
4372: . array - pointer to the data
4374: Level: intermediate
4376: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4377: @*/
4378: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar *array[])
4379: {
4380: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4382: PetscFunctionBegin;
4383: if (aij->ops->getarrayread) {
4384: PetscCall((*aij->ops->getarrayread)(A, array));
4385: } else {
4386: *array = aij->a;
4387: }
4388: PetscFunctionReturn(PETSC_SUCCESS);
4389: }
4391: /*@C
4392: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4394: Not Collective; No Fortran Support
4396: Input Parameter:
4397: . A - a `MATSEQAIJ` matrix
4399: Output Parameter:
4400: . array - pointer to the data
4402: Level: intermediate
4404: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4405: @*/
4406: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar *array[])
4407: {
4408: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4410: PetscFunctionBegin;
4411: if (aij->ops->restorearrayread) {
4412: PetscCall((*aij->ops->restorearrayread)(A, array));
4413: } else {
4414: *array = NULL;
4415: }
4416: PetscFunctionReturn(PETSC_SUCCESS);
4417: }
4419: /*@C
4420: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4422: Not Collective; No Fortran Support
4424: Input Parameter:
4425: . A - a `MATSEQAIJ` matrix
4427: Output Parameter:
4428: . array - pointer to the data
4430: Level: intermediate
4432: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4433: @*/
4434: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar *array[])
4435: {
4436: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4438: PetscFunctionBegin;
4439: if (aij->ops->getarraywrite) {
4440: PetscCall((*aij->ops->getarraywrite)(A, array));
4441: } else {
4442: *array = aij->a;
4443: }
4444: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4445: PetscFunctionReturn(PETSC_SUCCESS);
4446: }
4448: /*@C
4449: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4451: Not Collective; No Fortran Support
4453: Input Parameter:
4454: . A - a MATSEQAIJ matrix
4456: Output Parameter:
4457: . array - pointer to the data
4459: Level: intermediate
4461: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4462: @*/
4463: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar *array[])
4464: {
4465: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4467: PetscFunctionBegin;
4468: if (aij->ops->restorearraywrite) {
4469: PetscCall((*aij->ops->restorearraywrite)(A, array));
4470: } else {
4471: *array = NULL;
4472: }
4473: PetscFunctionReturn(PETSC_SUCCESS);
4474: }
4476: /*@C
4477: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4479: Not Collective; No Fortran Support
4481: Input Parameter:
4482: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4484: Output Parameters:
4485: + i - row map array of the matrix
4486: . j - column index array of the matrix
4487: . a - data array of the matrix
4488: - mtype - memory type of the arrays
4490: Level: developer
4492: Notes:
4493: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4494: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4496: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4497: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4499: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4500: @*/
4501: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt *i[], const PetscInt *j[], PetscScalar *a[], PetscMemType *mtype)
4502: {
4503: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4505: PetscFunctionBegin;
4506: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4507: if (aij->ops->getcsrandmemtype) {
4508: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4509: } else {
4510: if (i) *i = aij->i;
4511: if (j) *j = aij->j;
4512: if (a) *a = aij->a;
4513: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4514: }
4515: PetscFunctionReturn(PETSC_SUCCESS);
4516: }
4518: /*@
4519: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4521: Not Collective
4523: Input Parameter:
4524: . A - a `MATSEQAIJ` matrix
4526: Output Parameter:
4527: . nz - the maximum number of nonzeros in any row
4529: Level: intermediate
4531: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`
4532: @*/
4533: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4534: {
4535: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4537: PetscFunctionBegin;
4538: *nz = aij->rmax;
4539: PetscFunctionReturn(PETSC_SUCCESS);
4540: }
4542: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void **data)
4543: {
4544: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)*data;
4546: PetscFunctionBegin;
4547: PetscCall(PetscFree(coo->perm));
4548: PetscCall(PetscFree(coo->jmap));
4549: PetscCall(PetscFree(coo));
4550: PetscFunctionReturn(PETSC_SUCCESS);
4551: }
4553: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4554: {
4555: MPI_Comm comm;
4556: PetscInt *i, *j;
4557: PetscInt M, N, row, iprev;
4558: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4559: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4560: PetscInt *Aj;
4561: PetscScalar *Aa;
4562: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)mat->data;
4563: MatType rtype;
4564: PetscCount *perm, *jmap;
4565: MatCOOStruct_SeqAIJ *coo;
4566: PetscBool isorted;
4567: PetscBool hypre;
4569: PetscFunctionBegin;
4570: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4571: PetscCall(MatGetSize(mat, &M, &N));
4572: i = coo_i;
4573: j = coo_j;
4574: PetscCall(PetscMalloc1(coo_n, &perm));
4576: /* 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) */
4577: isorted = PETSC_TRUE;
4578: iprev = PETSC_INT_MIN;
4579: for (k = 0; k < coo_n; k++) {
4580: if (j[k] < 0) i[k] = -1;
4581: if (isorted) {
4582: if (i[k] < iprev) isorted = PETSC_FALSE;
4583: else iprev = i[k];
4584: }
4585: perm[k] = k;
4586: }
4588: /* Sort by row if not already */
4589: if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4590: PetscCheck(coo_n == 0 || i[coo_n - 1] < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "COO row index %" PetscInt_FMT " is >= the matrix row size %" PetscInt_FMT, i[coo_n - 1], M);
4592: /* Advance k to the first row with a non-negative index */
4593: for (k = 0; k < coo_n; k++)
4594: if (i[k] >= 0) break;
4595: nneg = k;
4596: 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 */
4597: nnz = 0; /* Total number of unique nonzeros to be counted */
4598: jmap++; /* Inc jmap by 1 for convenience */
4600: PetscCall(PetscShmgetAllocateArray(M + 1, sizeof(PetscInt), (void **)&Ai)); /* CSR of A */
4601: PetscCall(PetscArrayzero(Ai, M + 1));
4602: PetscCall(PetscShmgetAllocateArray(coo_n - nneg, sizeof(PetscInt), (void **)&Aj)); /* We have at most coo_n-nneg unique nonzeros */
4604: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
4606: /* In each row, sort by column, then unique column indices to get row length */
4607: Ai++; /* Inc by 1 for convenience */
4608: q = 0; /* q-th unique nonzero, with q starting from 0 */
4609: while (k < coo_n) {
4610: PetscBool strictly_sorted; // this row is strictly sorted?
4611: PetscInt jprev;
4613: /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4614: row = i[k];
4615: start = k;
4616: jprev = PETSC_INT_MIN;
4617: strictly_sorted = PETSC_TRUE;
4618: while (k < coo_n && i[k] == row) {
4619: if (strictly_sorted) {
4620: if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4621: else jprev = j[k];
4622: }
4623: k++;
4624: }
4625: end = k;
4627: /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4628: if (hypre) {
4629: PetscInt minj = PETSC_INT_MAX;
4630: PetscBool hasdiag = PETSC_FALSE;
4632: if (strictly_sorted) { // fast path to swap the first and the diag
4633: PetscCount tmp;
4634: for (p = start; p < end; p++) {
4635: if (j[p] == row && p != start) {
4636: j[p] = j[start]; // swap j[], so that the diagonal value will go first (manipulated by perm[])
4637: j[start] = row;
4638: tmp = perm[start];
4639: perm[start] = perm[p]; // also swap perm[] so we can save the call to PetscSortIntWithCountArray() below
4640: perm[p] = tmp;
4641: break;
4642: }
4643: }
4644: } else {
4645: for (p = start; p < end; p++) {
4646: hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4647: minj = PetscMin(minj, j[p]);
4648: }
4650: if (hasdiag) {
4651: for (p = start; p < end; p++) {
4652: if (j[p] == minj) j[p] = row;
4653: else if (j[p] == row) j[p] = minj;
4654: }
4655: }
4656: }
4657: }
4658: // sort by columns in a row. perm[] indicates their original order
4659: if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4660: PetscCheck(end == start || j[end - 1] < N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "COO column index %" PetscInt_FMT " is >= the matrix column size %" PetscInt_FMT, j[end - 1], N);
4662: if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4663: for (p = start; p < end; p++, q++) {
4664: Aj[q] = j[p];
4665: jmap[q] = 1;
4666: }
4667: PetscCall(PetscIntCast(end - start, Ai + row));
4668: nnz += Ai[row]; // q is already advanced
4669: } else {
4670: /* Find number of unique col entries in this row */
4671: Aj[q] = j[start]; /* Log the first nonzero in this row */
4672: jmap[q] = 1; /* Number of repeats of this nonzero entry */
4673: Ai[row] = 1;
4674: nnz++;
4676: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4677: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4678: q++;
4679: jmap[q] = 1;
4680: Aj[q] = j[p];
4681: Ai[row]++;
4682: nnz++;
4683: } else {
4684: jmap[q]++;
4685: }
4686: }
4687: q++; /* Move to next row and thus next unique nonzero */
4688: }
4689: }
4691: Ai--; /* Back to the beginning of Ai[] */
4692: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4693: jmap--; // Back to the beginning of jmap[]
4694: jmap[0] = 0;
4695: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4697: if (nnz < coo_n - nneg) { /* Reallocate with actual number of unique nonzeros */
4698: PetscCount *jmap_new;
4699: PetscInt *Aj_new;
4701: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4702: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4703: PetscCall(PetscFree(jmap));
4704: jmap = jmap_new;
4706: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscInt), (void **)&Aj_new));
4707: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4708: PetscCall(PetscShmgetDeallocateArray((void **)&Aj));
4709: Aj = Aj_new;
4710: }
4712: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4713: PetscCount *perm_new;
4715: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4716: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4717: PetscCall(PetscFree(perm));
4718: perm = perm_new;
4719: }
4721: PetscCall(MatGetRootType_Private(mat, &rtype));
4722: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscScalar), (void **)&Aa));
4723: PetscCall(PetscArrayzero(Aa, nnz));
4724: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4726: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4728: // Put the COO struct in a container and then attach that to the matrix
4729: PetscCall(PetscMalloc1(1, &coo));
4730: PetscCall(PetscIntCast(nnz, &coo->nz));
4731: coo->n = coo_n;
4732: coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4733: coo->jmap = jmap; // of length nnz+1
4734: coo->perm = perm;
4735: PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", coo, MatCOOStructDestroy_SeqAIJ));
4736: PetscFunctionReturn(PETSC_SUCCESS);
4737: }
4739: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4740: {
4741: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4742: PetscCount i, j, Annz = aseq->nz;
4743: PetscCount *perm, *jmap;
4744: PetscScalar *Aa;
4745: PetscContainer container;
4746: MatCOOStruct_SeqAIJ *coo;
4748: PetscFunctionBegin;
4749: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4750: PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4751: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4752: perm = coo->perm;
4753: jmap = coo->jmap;
4754: PetscCall(MatSeqAIJGetArray(A, &Aa));
4755: for (i = 0; i < Annz; i++) {
4756: PetscScalar sum = 0.0;
4757: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4758: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4759: }
4760: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4761: PetscFunctionReturn(PETSC_SUCCESS);
4762: }
4764: #if defined(PETSC_HAVE_CUDA)
4765: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4766: #endif
4767: #if defined(PETSC_HAVE_HIP)
4768: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4769: #endif
4770: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4771: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4772: #endif
4774: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4775: {
4776: Mat_SeqAIJ *b;
4777: PetscMPIInt size;
4779: PetscFunctionBegin;
4780: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4781: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4783: PetscCall(PetscNew(&b));
4785: B->data = (void *)b;
4786: B->ops[0] = MatOps_Values;
4787: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4789: b->row = NULL;
4790: b->col = NULL;
4791: b->icol = NULL;
4792: b->reallocs = 0;
4793: b->ignorezeroentries = PETSC_FALSE;
4794: b->roworiented = PETSC_TRUE;
4795: b->nonew = 0;
4796: b->diag = NULL;
4797: b->solve_work = NULL;
4798: B->spptr = NULL;
4799: b->saved_values = NULL;
4800: b->idiag = NULL;
4801: b->mdiag = NULL;
4802: b->ssor_work = NULL;
4803: b->omega = 1.0;
4804: b->fshift = 0.0;
4805: b->ibdiagvalid = PETSC_FALSE;
4806: b->keepnonzeropattern = PETSC_FALSE;
4808: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4809: #if defined(PETSC_HAVE_MATLAB)
4810: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4811: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4812: #endif
4813: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4814: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4815: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4816: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4817: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4818: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4819: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4820: #if defined(PETSC_HAVE_MKL_SPARSE)
4821: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4822: #endif
4823: #if defined(PETSC_HAVE_CUDA)
4824: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4825: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4826: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4827: #endif
4828: #if defined(PETSC_HAVE_HIP)
4829: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4830: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4831: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4832: #endif
4833: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4834: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4835: #endif
4836: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4837: #if defined(PETSC_HAVE_ELEMENTAL)
4838: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4839: #endif
4840: #if defined(PETSC_HAVE_SCALAPACK) && (defined(PETSC_USE_REAL_SINGLE) || defined(PETSC_USE_REAL_DOUBLE))
4841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4842: #endif
4843: #if defined(PETSC_HAVE_HYPRE)
4844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4845: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4846: #endif
4847: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4848: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4849: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4850: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4851: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4852: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4853: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4854: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_SeqAIJ));
4855: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4856: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4857: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4858: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4859: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4860: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4861: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4862: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4863: PetscCall(MatCreate_SeqAIJ_Inode(B));
4864: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4865: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4866: PetscFunctionReturn(PETSC_SUCCESS);
4867: }
4869: /*
4870: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4871: */
4872: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4873: {
4874: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4875: PetscInt m = A->rmap->n, i;
4877: PetscFunctionBegin;
4878: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4880: C->factortype = A->factortype;
4881: c->row = NULL;
4882: c->col = NULL;
4883: c->icol = NULL;
4884: c->reallocs = 0;
4885: C->assembled = A->assembled;
4887: if (A->preallocated) {
4888: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4889: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4891: if (!A->hash_active) {
4892: PetscCall(PetscMalloc1(m, &c->imax));
4893: PetscCall(PetscArraycpy(c->imax, a->imax, m));
4894: PetscCall(PetscMalloc1(m, &c->ilen));
4895: PetscCall(PetscArraycpy(c->ilen, a->ilen, m));
4897: /* allocate the matrix space */
4898: if (mallocmatspace) {
4899: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscScalar), (void **)&c->a));
4900: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscInt), (void **)&c->j));
4901: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4902: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
4903: c->free_a = PETSC_TRUE;
4904: c->free_ij = PETSC_TRUE;
4905: if (m > 0) {
4906: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
4907: if (cpvalues == MAT_COPY_VALUES) {
4908: const PetscScalar *aa;
4910: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4911: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
4912: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4913: } else {
4914: PetscCall(PetscArrayzero(c->a, a->i[m]));
4915: }
4916: }
4917: }
4918: C->preallocated = PETSC_TRUE;
4919: } else {
4920: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
4921: PetscCall(MatSetUp(C));
4922: }
4924: c->ignorezeroentries = a->ignorezeroentries;
4925: c->roworiented = a->roworiented;
4926: c->nonew = a->nonew;
4927: c->solve_work = NULL;
4928: c->saved_values = NULL;
4929: c->idiag = NULL;
4930: c->ssor_work = NULL;
4931: c->keepnonzeropattern = a->keepnonzeropattern;
4933: c->rmax = a->rmax;
4934: c->nz = a->nz;
4935: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4937: c->compressedrow.use = a->compressedrow.use;
4938: c->compressedrow.nrows = a->compressedrow.nrows;
4939: if (a->compressedrow.use) {
4940: i = a->compressedrow.nrows;
4941: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
4942: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
4943: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
4944: } else {
4945: c->compressedrow.use = PETSC_FALSE;
4946: c->compressedrow.i = NULL;
4947: c->compressedrow.rindex = NULL;
4948: }
4949: c->nonzerorowcnt = a->nonzerorowcnt;
4950: C->nonzerostate = A->nonzerostate;
4952: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
4953: }
4954: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
4955: PetscFunctionReturn(PETSC_SUCCESS);
4956: }
4958: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
4959: {
4960: PetscFunctionBegin;
4961: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
4962: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
4963: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
4964: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
4965: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
4966: PetscFunctionReturn(PETSC_SUCCESS);
4967: }
4969: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4970: {
4971: PetscBool isbinary, ishdf5;
4973: PetscFunctionBegin;
4976: /* force binary viewer to load .info file if it has not yet done so */
4977: PetscCall(PetscViewerSetUp(viewer));
4978: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
4979: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
4980: if (isbinary) {
4981: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
4982: } else if (ishdf5) {
4983: #if defined(PETSC_HAVE_HDF5)
4984: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
4985: #else
4986: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4987: #endif
4988: } else {
4989: 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);
4990: }
4991: PetscFunctionReturn(PETSC_SUCCESS);
4992: }
4994: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4995: {
4996: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
4997: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
4999: PetscFunctionBegin;
5000: PetscCall(PetscViewerSetUp(viewer));
5002: /* read in matrix header */
5003: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5004: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5005: M = header[1];
5006: N = header[2];
5007: nz = header[3];
5008: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5009: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5010: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5012: /* set block sizes from the viewer's .info file */
5013: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5014: /* set local and global sizes if not set already */
5015: if (mat->rmap->n < 0) mat->rmap->n = M;
5016: if (mat->cmap->n < 0) mat->cmap->n = N;
5017: if (mat->rmap->N < 0) mat->rmap->N = M;
5018: if (mat->cmap->N < 0) mat->cmap->N = N;
5019: PetscCall(PetscLayoutSetUp(mat->rmap));
5020: PetscCall(PetscLayoutSetUp(mat->cmap));
5022: /* check if the matrix sizes are correct */
5023: PetscCall(MatGetSize(mat, &rows, &cols));
5024: 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);
5026: /* read in row lengths */
5027: PetscCall(PetscMalloc1(M, &rowlens));
5028: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5029: /* check if sum(rowlens) is same as nz */
5030: sum = 0;
5031: for (i = 0; i < M; i++) sum += rowlens[i];
5032: 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);
5033: /* preallocate and check sizes */
5034: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5035: PetscCall(MatGetSize(mat, &rows, &cols));
5036: 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);
5037: /* store row lengths */
5038: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5039: PetscCall(PetscFree(rowlens));
5041: /* fill in "i" row pointers */
5042: a->i[0] = 0;
5043: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5044: /* read in "j" column indices */
5045: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5046: /* read in "a" nonzero values */
5047: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5049: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5050: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5051: PetscFunctionReturn(PETSC_SUCCESS);
5052: }
5054: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5055: {
5056: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5057: const PetscScalar *aa, *ba;
5059: PetscFunctionBegin;
5060: /* If the matrix dimensions are not equal,or no of nonzeros */
5061: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5062: *flg = PETSC_FALSE;
5063: PetscFunctionReturn(PETSC_SUCCESS);
5064: }
5066: /* if the a->i are the same */
5067: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5068: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5070: /* if a->j are the same */
5071: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5072: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5074: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5075: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5076: /* if a->a are the same */
5077: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5078: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5079: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5080: PetscFunctionReturn(PETSC_SUCCESS);
5081: }
5083: /*@
5084: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5085: provided by the user.
5087: Collective
5089: Input Parameters:
5090: + comm - must be an MPI communicator of size 1
5091: . m - number of rows
5092: . n - number of columns
5093: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5094: . j - column indices
5095: - a - matrix values
5097: Output Parameter:
5098: . mat - the matrix
5100: Level: intermediate
5102: Notes:
5103: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5104: once the matrix is destroyed and not before
5106: You cannot set new nonzero locations into this matrix, that will generate an error.
5108: The `i` and `j` indices are 0 based
5110: The format which is used for the sparse matrix input, is equivalent to a
5111: row-major ordering.. i.e for the following matrix, the input data expected is
5112: as shown
5113: .vb
5114: 1 0 0
5115: 2 0 3
5116: 4 5 6
5118: i = {0,1,3,6} [size = nrow+1 = 3+1]
5119: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5120: v = {1,2,3,4,5,6} [size = 6]
5121: .ve
5123: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5124: @*/
5125: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5126: {
5127: PetscInt ii;
5128: Mat_SeqAIJ *aij;
5129: PetscInt jj;
5131: PetscFunctionBegin;
5132: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5133: PetscCall(MatCreate(comm, mat));
5134: PetscCall(MatSetSizes(*mat, m, n, m, n));
5135: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5136: PetscCall(MatSetType(*mat, MATSEQAIJ));
5137: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5138: aij = (Mat_SeqAIJ *)(*mat)->data;
5139: PetscCall(PetscMalloc1(m, &aij->imax));
5140: PetscCall(PetscMalloc1(m, &aij->ilen));
5142: aij->i = i;
5143: aij->j = j;
5144: aij->a = a;
5145: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5146: aij->free_a = PETSC_FALSE;
5147: aij->free_ij = PETSC_FALSE;
5149: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5150: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5151: if (PetscDefined(USE_DEBUG)) {
5152: 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]);
5153: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5154: 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);
5155: 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);
5156: }
5157: }
5158: }
5159: if (PetscDefined(USE_DEBUG)) {
5160: for (ii = 0; ii < aij->i[m]; ii++) {
5161: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5162: 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);
5163: }
5164: }
5166: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5167: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5168: PetscFunctionReturn(PETSC_SUCCESS);
5169: }
5171: /*@
5172: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5173: provided by the user.
5175: Collective
5177: Input Parameters:
5178: + comm - must be an MPI communicator of size 1
5179: . m - number of rows
5180: . n - number of columns
5181: . i - row indices
5182: . j - column indices
5183: . a - matrix values
5184: . nz - number of nonzeros
5185: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5187: Output Parameter:
5188: . mat - the matrix
5190: Level: intermediate
5192: Example:
5193: For the following matrix, the input data expected is as shown (using 0 based indexing)
5194: .vb
5195: 1 0 0
5196: 2 0 3
5197: 4 5 6
5199: i = {0,1,1,2,2,2}
5200: j = {0,0,2,0,1,2}
5201: v = {1,2,3,4,5,6}
5202: .ve
5204: Note:
5205: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5206: and are particularly useful in iterative applications.
5208: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5209: @*/
5210: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscCount nz, PetscBool idx)
5211: {
5212: PetscInt ii, *nnz, one = 1, row, col;
5214: PetscFunctionBegin;
5215: PetscCall(PetscCalloc1(m, &nnz));
5216: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5217: PetscCall(MatCreate(comm, mat));
5218: PetscCall(MatSetSizes(*mat, m, n, m, n));
5219: PetscCall(MatSetType(*mat, MATSEQAIJ));
5220: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5221: for (ii = 0; ii < nz; ii++) {
5222: if (idx) {
5223: row = i[ii] - 1;
5224: col = j[ii] - 1;
5225: } else {
5226: row = i[ii];
5227: col = j[ii];
5228: }
5229: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5230: }
5231: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5232: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5233: PetscCall(PetscFree(nnz));
5234: PetscFunctionReturn(PETSC_SUCCESS);
5235: }
5237: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5238: {
5239: PetscFunctionBegin;
5240: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5241: PetscFunctionReturn(PETSC_SUCCESS);
5242: }
5244: /*
5245: Permute A into C's *local* index space using rowemb,colemb.
5246: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5247: of [0,m), colemb is in [0,n).
5248: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5249: */
5250: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5251: {
5252: /* If making this function public, change the error returned in this function away from _PLIB. */
5253: Mat_SeqAIJ *Baij;
5254: PetscBool seqaij;
5255: PetscInt m, n, *nz, i, j, count;
5256: PetscScalar v;
5257: const PetscInt *rowindices, *colindices;
5259: PetscFunctionBegin;
5260: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5261: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5262: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5263: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5264: if (rowemb) {
5265: PetscCall(ISGetLocalSize(rowemb, &m));
5266: 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);
5267: } else {
5268: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5269: }
5270: if (colemb) {
5271: PetscCall(ISGetLocalSize(colemb, &n));
5272: 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);
5273: } else {
5274: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5275: }
5277: Baij = (Mat_SeqAIJ *)B->data;
5278: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5279: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5280: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5281: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5282: PetscCall(PetscFree(nz));
5283: }
5284: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5285: count = 0;
5286: rowindices = NULL;
5287: colindices = NULL;
5288: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5289: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5290: for (i = 0; i < B->rmap->n; i++) {
5291: PetscInt row;
5292: row = i;
5293: if (rowindices) row = rowindices[i];
5294: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5295: PetscInt col;
5296: col = Baij->j[count];
5297: if (colindices) col = colindices[col];
5298: v = Baij->a[count];
5299: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5300: ++count;
5301: }
5302: }
5303: /* FIXME: set C's nonzerostate correctly. */
5304: /* Assembly for C is necessary. */
5305: C->preallocated = PETSC_TRUE;
5306: C->assembled = PETSC_TRUE;
5307: C->was_assembled = PETSC_FALSE;
5308: PetscFunctionReturn(PETSC_SUCCESS);
5309: }
5311: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5312: {
5313: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5314: MatScalar *aa = a->a;
5315: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5316: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5318: PetscFunctionBegin;
5319: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5320: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5321: for (i = 1; i <= m; i++) {
5322: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5323: for (k = ai[i - 1]; k < ai[i]; k++) {
5324: if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5325: else {
5326: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5327: aa[k - fshift] = aa[k];
5328: aj[k - fshift] = aj[k];
5329: }
5330: }
5331: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5332: fshift_prev = fshift;
5333: /* reset ilen and imax for each row */
5334: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5335: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5336: rmax = PetscMax(rmax, ailen[i - 1]);
5337: }
5338: if (fshift) {
5339: if (m) {
5340: ai[m] -= fshift;
5341: a->nz = ai[m];
5342: }
5343: 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));
5344: A->nonzerostate++;
5345: A->info.nz_unneeded += (PetscReal)fshift;
5346: a->rmax = rmax;
5347: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5348: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5349: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5350: }
5351: PetscFunctionReturn(PETSC_SUCCESS);
5352: }
5354: PetscFunctionList MatSeqAIJList = NULL;
5356: /*@
5357: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5359: Collective
5361: Input Parameters:
5362: + mat - the matrix object
5363: - matype - matrix type
5365: Options Database Key:
5366: . -mat_seqaij_type <method> - for example seqaijcrl
5368: Level: intermediate
5370: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5371: @*/
5372: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5373: {
5374: PetscBool sametype;
5375: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5377: PetscFunctionBegin;
5379: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5380: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5382: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5383: PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5384: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5385: PetscFunctionReturn(PETSC_SUCCESS);
5386: }
5388: /*@C
5389: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5391: Not Collective, No Fortran Support
5393: Input Parameters:
5394: + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5395: - function - routine to convert to subtype
5397: Level: advanced
5399: Notes:
5400: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5402: Then, your matrix can be chosen with the procedural interface at runtime via the option
5403: .vb
5404: -mat_seqaij_type my_mat
5405: .ve
5407: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5408: @*/
5409: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5410: {
5411: PetscFunctionBegin;
5412: PetscCall(MatInitializePackage());
5413: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5414: PetscFunctionReturn(PETSC_SUCCESS);
5415: }
5417: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5419: /*@C
5420: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5422: Not Collective
5424: Level: advanced
5426: Note:
5427: This registers the versions of `MATSEQAIJ` for GPUs
5429: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5430: @*/
5431: PetscErrorCode MatSeqAIJRegisterAll(void)
5432: {
5433: PetscFunctionBegin;
5434: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5435: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5437: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5438: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5439: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5440: #if defined(PETSC_HAVE_MKL_SPARSE)
5441: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5442: #endif
5443: #if defined(PETSC_HAVE_CUDA)
5444: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5445: #endif
5446: #if defined(PETSC_HAVE_HIP)
5447: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5448: #endif
5449: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5450: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5451: #endif
5452: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5453: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5454: #endif
5455: PetscFunctionReturn(PETSC_SUCCESS);
5456: }
5458: /*
5459: Special version for direct calls from Fortran
5460: */
5461: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5462: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5463: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5464: #define matsetvaluesseqaij_ matsetvaluesseqaij
5465: #endif
5467: /* Change these macros so can be used in void function */
5469: /* Change these macros so can be used in void function */
5470: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5471: #undef PetscCall
5472: #define PetscCall(...) \
5473: do { \
5474: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5475: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5476: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5477: return; \
5478: } \
5479: } while (0)
5481: #undef SETERRQ
5482: #define SETERRQ(comm, ierr, ...) \
5483: do { \
5484: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5485: return; \
5486: } while (0)
5488: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5489: {
5490: Mat A = *AA;
5491: PetscInt m = *mm, n = *nn;
5492: InsertMode is = *isis;
5493: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5494: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5495: PetscInt *imax, *ai, *ailen;
5496: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5497: MatScalar *ap, value, *aa;
5498: PetscBool ignorezeroentries = a->ignorezeroentries;
5499: PetscBool roworiented = a->roworiented;
5501: PetscFunctionBegin;
5502: MatCheckPreallocated(A, 1);
5503: imax = a->imax;
5504: ai = a->i;
5505: ailen = a->ilen;
5506: aj = a->j;
5507: aa = a->a;
5509: for (k = 0; k < m; k++) { /* loop over added rows */
5510: row = im[k];
5511: if (row < 0) continue;
5512: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5513: rp = aj + ai[row];
5514: ap = aa + ai[row];
5515: rmax = imax[row];
5516: nrow = ailen[row];
5517: low = 0;
5518: high = nrow;
5519: for (l = 0; l < n; l++) { /* loop over added columns */
5520: if (in[l] < 0) continue;
5521: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5522: col = in[l];
5523: if (roworiented) value = v[l + k * n];
5524: else value = v[k + l * m];
5526: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5528: if (col <= lastcol) low = 0;
5529: else high = nrow;
5530: lastcol = col;
5531: while (high - low > 5) {
5532: t = (low + high) / 2;
5533: if (rp[t] > col) high = t;
5534: else low = t;
5535: }
5536: for (i = low; i < high; i++) {
5537: if (rp[i] > col) break;
5538: if (rp[i] == col) {
5539: if (is == ADD_VALUES) ap[i] += value;
5540: else ap[i] = value;
5541: goto noinsert;
5542: }
5543: }
5544: if (value == 0.0 && ignorezeroentries) goto noinsert;
5545: if (nonew == 1) goto noinsert;
5546: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5547: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5548: N = nrow++ - 1;
5549: a->nz++;
5550: high++;
5551: /* shift up all the later entries in this row */
5552: for (ii = N; ii >= i; ii--) {
5553: rp[ii + 1] = rp[ii];
5554: ap[ii + 1] = ap[ii];
5555: }
5556: rp[i] = col;
5557: ap[i] = value;
5558: noinsert:;
5559: low = i + 1;
5560: }
5561: ailen[row] = nrow;
5562: }
5563: PetscFunctionReturnVoid();
5564: }
5565: /* Undefining these here since they were redefined from their original definition above! No
5566: * other PETSc functions should be defined past this point, as it is impossible to recover the
5567: * original definitions */
5568: #undef PetscCall
5569: #undef SETERRQ