Actual source code: baij.c
1: /*
2: Defines the basic matrix operations for the BAIJ (compressed row)
3: matrix storage format.
4: */
5: #include <../src/mat/impls/baij/seq/baij.h>
6: #include <petscblaslapack.h>
7: #include <petsc/private/kernels/blockinvert.h>
8: #include <petsc/private/kernels/blockmatmult.h>
10: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
11: #define TYPE BAIJ
12: #define TYPE_BS
13: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
14: #undef TYPE_BS
15: #define TYPE_BS _BS
16: #define TYPE_BS_ON
17: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
18: #undef TYPE_BS
19: #include "../src/mat/impls/aij/seq/seqhashmat.h"
20: #undef TYPE
21: #undef TYPE_BS_ON
23: #if defined(PETSC_HAVE_HYPRE)
24: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
25: #endif
27: #if defined(PETSC_HAVE_MKL_SPARSE_OPTIMIZE)
28: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat, MatType, MatReuse, Mat *);
29: #endif
30: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
32: static PetscErrorCode MatGetColumnReductions_SeqBAIJ(Mat A, PetscInt type, PetscReal *reductions)
33: {
34: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)A->data;
35: PetscInt m, n, ib, jb, bs = A->rmap->bs;
36: MatScalar *a_val = a_aij->a;
38: PetscFunctionBegin;
39: PetscCall(MatGetSize(A, &m, &n));
40: PetscCall(PetscArrayzero(reductions, n));
41: if (type == NORM_2) {
42: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
43: for (jb = 0; jb < bs; jb++) {
44: for (ib = 0; ib < bs; ib++) {
45: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
46: a_val++;
47: }
48: }
49: }
50: } else if (type == NORM_1) {
51: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
52: for (jb = 0; jb < bs; jb++) {
53: for (ib = 0; ib < bs; ib++) {
54: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
55: a_val++;
56: }
57: }
58: }
59: } else if (type == NORM_INFINITY) {
60: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
61: for (jb = 0; jb < bs; jb++) {
62: for (ib = 0; ib < bs; ib++) {
63: PetscInt col = A->cmap->rstart + a_aij->j[i] * bs + jb;
64: reductions[col] = PetscMax(PetscAbsScalar(*a_val), reductions[col]);
65: a_val++;
66: }
67: }
68: }
69: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
70: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
71: for (jb = 0; jb < bs; jb++) {
72: for (ib = 0; ib < bs; ib++) {
73: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
74: a_val++;
75: }
76: }
77: }
78: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
79: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
80: for (jb = 0; jb < bs; jb++) {
81: for (ib = 0; ib < bs; ib++) {
82: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
83: a_val++;
84: }
85: }
86: }
87: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
88: if (type == NORM_2) {
89: for (PetscInt i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
90: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
91: for (PetscInt i = 0; i < n; i++) reductions[i] /= m;
92: }
93: PetscFunctionReturn(PETSC_SUCCESS);
94: }
96: static PetscErrorCode MatInvertBlockDiagonal_SeqBAIJ(Mat A, const PetscScalar **values)
97: {
98: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
99: PetscInt *diag_offset, i, bs = A->rmap->bs, mbs = a->mbs, ipvt[5], bs2 = bs * bs, *v_pivots;
100: MatScalar *v = a->a, *odiag, *diag, work[25], *v_work;
101: PetscReal shift = 0.0;
102: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
104: PetscFunctionBegin;
105: allowzeropivot = PetscNot(A->erroriffailure);
107: if (a->idiagvalid) {
108: if (values) *values = a->idiag;
109: PetscFunctionReturn(PETSC_SUCCESS);
110: }
111: PetscCall(MatMarkDiagonal_SeqBAIJ(A));
112: diag_offset = a->diag;
113: if (!a->idiag) PetscCall(PetscMalloc1(bs2 * mbs, &a->idiag));
114: diag = a->idiag;
115: if (values) *values = a->idiag;
116: /* factor and invert each block */
117: switch (bs) {
118: case 1:
119: for (i = 0; i < mbs; i++) {
120: odiag = v + 1 * diag_offset[i];
121: diag[0] = odiag[0];
123: if (PetscAbsScalar(diag[0] + shift) < PETSC_MACHINE_EPSILON) {
124: PetscCheck(allowzeropivot, PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot value %g tolerance %g", i, (double)PetscAbsScalar(diag[0]), (double)PETSC_MACHINE_EPSILON);
125: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
126: A->factorerror_zeropivot_value = PetscAbsScalar(diag[0]);
127: A->factorerror_zeropivot_row = i;
128: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT "\n", i));
129: }
131: diag[0] = (PetscScalar)1.0 / (diag[0] + shift);
132: diag += 1;
133: }
134: break;
135: case 2:
136: for (i = 0; i < mbs; i++) {
137: odiag = v + 4 * diag_offset[i];
138: diag[0] = odiag[0];
139: diag[1] = odiag[1];
140: diag[2] = odiag[2];
141: diag[3] = odiag[3];
142: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
143: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
144: diag += 4;
145: }
146: break;
147: case 3:
148: for (i = 0; i < mbs; i++) {
149: odiag = v + 9 * diag_offset[i];
150: diag[0] = odiag[0];
151: diag[1] = odiag[1];
152: diag[2] = odiag[2];
153: diag[3] = odiag[3];
154: diag[4] = odiag[4];
155: diag[5] = odiag[5];
156: diag[6] = odiag[6];
157: diag[7] = odiag[7];
158: diag[8] = odiag[8];
159: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
160: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
161: diag += 9;
162: }
163: break;
164: case 4:
165: for (i = 0; i < mbs; i++) {
166: odiag = v + 16 * diag_offset[i];
167: PetscCall(PetscArraycpy(diag, odiag, 16));
168: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
169: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
170: diag += 16;
171: }
172: break;
173: case 5:
174: for (i = 0; i < mbs; i++) {
175: odiag = v + 25 * diag_offset[i];
176: PetscCall(PetscArraycpy(diag, odiag, 25));
177: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
178: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
179: diag += 25;
180: }
181: break;
182: case 6:
183: for (i = 0; i < mbs; i++) {
184: odiag = v + 36 * diag_offset[i];
185: PetscCall(PetscArraycpy(diag, odiag, 36));
186: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
187: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
188: diag += 36;
189: }
190: break;
191: case 7:
192: for (i = 0; i < mbs; i++) {
193: odiag = v + 49 * diag_offset[i];
194: PetscCall(PetscArraycpy(diag, odiag, 49));
195: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
196: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
197: diag += 49;
198: }
199: break;
200: default:
201: PetscCall(PetscMalloc2(bs, &v_work, bs, &v_pivots));
202: for (i = 0; i < mbs; i++) {
203: odiag = v + bs2 * diag_offset[i];
204: PetscCall(PetscArraycpy(diag, odiag, bs2));
205: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
206: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
207: diag += bs2;
208: }
209: PetscCall(PetscFree2(v_work, v_pivots));
210: }
211: a->idiagvalid = PETSC_TRUE;
212: PetscFunctionReturn(PETSC_SUCCESS);
213: }
215: static PetscErrorCode MatSOR_SeqBAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
216: {
217: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
218: PetscScalar *x, *work, *w, *workt, *t;
219: const MatScalar *v, *aa = a->a, *idiag;
220: const PetscScalar *b, *xb;
221: PetscScalar s[7], xw[7] = {0}; /* avoid some compilers thinking xw is uninitialized */
222: PetscInt m = a->mbs, i, i2, nz, bs = A->rmap->bs, bs2 = bs * bs, k, j, idx, it;
223: const PetscInt *diag, *ai = a->i, *aj = a->j, *vi;
225: PetscFunctionBegin;
226: its = its * lits;
227: PetscCheck(!(flag & SOR_EISENSTAT), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support yet for Eisenstat");
228: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " and local its %" PetscInt_FMT " both positive", its, lits);
229: PetscCheck(!fshift, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for diagonal shift");
230: PetscCheck(omega == 1.0, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for non-trivial relaxation factor");
231: PetscCheck(!(flag & SOR_APPLY_UPPER) && !(flag & SOR_APPLY_LOWER), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for applying upper or lower triangular parts");
233: if (!a->idiagvalid) PetscCall(MatInvertBlockDiagonal(A, NULL));
235: if (!m) PetscFunctionReturn(PETSC_SUCCESS);
236: diag = a->diag;
237: idiag = a->idiag;
238: k = PetscMax(A->rmap->n, A->cmap->n);
239: if (!a->mult_work) PetscCall(PetscMalloc1(k + 1, &a->mult_work));
240: if (!a->sor_workt) PetscCall(PetscMalloc1(k, &a->sor_workt));
241: if (!a->sor_work) PetscCall(PetscMalloc1(bs, &a->sor_work));
242: work = a->mult_work;
243: t = a->sor_workt;
244: w = a->sor_work;
246: PetscCall(VecGetArray(xx, &x));
247: PetscCall(VecGetArrayRead(bb, &b));
249: if (flag & SOR_ZERO_INITIAL_GUESS) {
250: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
251: switch (bs) {
252: case 1:
253: PetscKernel_v_gets_A_times_w_1(x, idiag, b);
254: t[0] = b[0];
255: i2 = 1;
256: idiag += 1;
257: for (i = 1; i < m; i++) {
258: v = aa + ai[i];
259: vi = aj + ai[i];
260: nz = diag[i] - ai[i];
261: s[0] = b[i2];
262: for (j = 0; j < nz; j++) {
263: xw[0] = x[vi[j]];
264: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
265: }
266: t[i2] = s[0];
267: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
268: x[i2] = xw[0];
269: idiag += 1;
270: i2 += 1;
271: }
272: break;
273: case 2:
274: PetscKernel_v_gets_A_times_w_2(x, idiag, b);
275: t[0] = b[0];
276: t[1] = b[1];
277: i2 = 2;
278: idiag += 4;
279: for (i = 1; i < m; i++) {
280: v = aa + 4 * ai[i];
281: vi = aj + ai[i];
282: nz = diag[i] - ai[i];
283: s[0] = b[i2];
284: s[1] = b[i2 + 1];
285: for (j = 0; j < nz; j++) {
286: idx = 2 * vi[j];
287: it = 4 * j;
288: xw[0] = x[idx];
289: xw[1] = x[1 + idx];
290: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
291: }
292: t[i2] = s[0];
293: t[i2 + 1] = s[1];
294: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
295: x[i2] = xw[0];
296: x[i2 + 1] = xw[1];
297: idiag += 4;
298: i2 += 2;
299: }
300: break;
301: case 3:
302: PetscKernel_v_gets_A_times_w_3(x, idiag, b);
303: t[0] = b[0];
304: t[1] = b[1];
305: t[2] = b[2];
306: i2 = 3;
307: idiag += 9;
308: for (i = 1; i < m; i++) {
309: v = aa + 9 * ai[i];
310: vi = aj + ai[i];
311: nz = diag[i] - ai[i];
312: s[0] = b[i2];
313: s[1] = b[i2 + 1];
314: s[2] = b[i2 + 2];
315: while (nz--) {
316: idx = 3 * (*vi++);
317: xw[0] = x[idx];
318: xw[1] = x[1 + idx];
319: xw[2] = x[2 + idx];
320: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
321: v += 9;
322: }
323: t[i2] = s[0];
324: t[i2 + 1] = s[1];
325: t[i2 + 2] = s[2];
326: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
327: x[i2] = xw[0];
328: x[i2 + 1] = xw[1];
329: x[i2 + 2] = xw[2];
330: idiag += 9;
331: i2 += 3;
332: }
333: break;
334: case 4:
335: PetscKernel_v_gets_A_times_w_4(x, idiag, b);
336: t[0] = b[0];
337: t[1] = b[1];
338: t[2] = b[2];
339: t[3] = b[3];
340: i2 = 4;
341: idiag += 16;
342: for (i = 1; i < m; i++) {
343: v = aa + 16 * ai[i];
344: vi = aj + ai[i];
345: nz = diag[i] - ai[i];
346: s[0] = b[i2];
347: s[1] = b[i2 + 1];
348: s[2] = b[i2 + 2];
349: s[3] = b[i2 + 3];
350: while (nz--) {
351: idx = 4 * (*vi++);
352: xw[0] = x[idx];
353: xw[1] = x[1 + idx];
354: xw[2] = x[2 + idx];
355: xw[3] = x[3 + idx];
356: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
357: v += 16;
358: }
359: t[i2] = s[0];
360: t[i2 + 1] = s[1];
361: t[i2 + 2] = s[2];
362: t[i2 + 3] = s[3];
363: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
364: x[i2] = xw[0];
365: x[i2 + 1] = xw[1];
366: x[i2 + 2] = xw[2];
367: x[i2 + 3] = xw[3];
368: idiag += 16;
369: i2 += 4;
370: }
371: break;
372: case 5:
373: PetscKernel_v_gets_A_times_w_5(x, idiag, b);
374: t[0] = b[0];
375: t[1] = b[1];
376: t[2] = b[2];
377: t[3] = b[3];
378: t[4] = b[4];
379: i2 = 5;
380: idiag += 25;
381: for (i = 1; i < m; i++) {
382: v = aa + 25 * ai[i];
383: vi = aj + ai[i];
384: nz = diag[i] - ai[i];
385: s[0] = b[i2];
386: s[1] = b[i2 + 1];
387: s[2] = b[i2 + 2];
388: s[3] = b[i2 + 3];
389: s[4] = b[i2 + 4];
390: while (nz--) {
391: idx = 5 * (*vi++);
392: xw[0] = x[idx];
393: xw[1] = x[1 + idx];
394: xw[2] = x[2 + idx];
395: xw[3] = x[3 + idx];
396: xw[4] = x[4 + idx];
397: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
398: v += 25;
399: }
400: t[i2] = s[0];
401: t[i2 + 1] = s[1];
402: t[i2 + 2] = s[2];
403: t[i2 + 3] = s[3];
404: t[i2 + 4] = s[4];
405: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
406: x[i2] = xw[0];
407: x[i2 + 1] = xw[1];
408: x[i2 + 2] = xw[2];
409: x[i2 + 3] = xw[3];
410: x[i2 + 4] = xw[4];
411: idiag += 25;
412: i2 += 5;
413: }
414: break;
415: case 6:
416: PetscKernel_v_gets_A_times_w_6(x, idiag, b);
417: t[0] = b[0];
418: t[1] = b[1];
419: t[2] = b[2];
420: t[3] = b[3];
421: t[4] = b[4];
422: t[5] = b[5];
423: i2 = 6;
424: idiag += 36;
425: for (i = 1; i < m; i++) {
426: v = aa + 36 * ai[i];
427: vi = aj + ai[i];
428: nz = diag[i] - ai[i];
429: s[0] = b[i2];
430: s[1] = b[i2 + 1];
431: s[2] = b[i2 + 2];
432: s[3] = b[i2 + 3];
433: s[4] = b[i2 + 4];
434: s[5] = b[i2 + 5];
435: while (nz--) {
436: idx = 6 * (*vi++);
437: xw[0] = x[idx];
438: xw[1] = x[1 + idx];
439: xw[2] = x[2 + idx];
440: xw[3] = x[3 + idx];
441: xw[4] = x[4 + idx];
442: xw[5] = x[5 + idx];
443: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
444: v += 36;
445: }
446: t[i2] = s[0];
447: t[i2 + 1] = s[1];
448: t[i2 + 2] = s[2];
449: t[i2 + 3] = s[3];
450: t[i2 + 4] = s[4];
451: t[i2 + 5] = s[5];
452: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
453: x[i2] = xw[0];
454: x[i2 + 1] = xw[1];
455: x[i2 + 2] = xw[2];
456: x[i2 + 3] = xw[3];
457: x[i2 + 4] = xw[4];
458: x[i2 + 5] = xw[5];
459: idiag += 36;
460: i2 += 6;
461: }
462: break;
463: case 7:
464: PetscKernel_v_gets_A_times_w_7(x, idiag, b);
465: t[0] = b[0];
466: t[1] = b[1];
467: t[2] = b[2];
468: t[3] = b[3];
469: t[4] = b[4];
470: t[5] = b[5];
471: t[6] = b[6];
472: i2 = 7;
473: idiag += 49;
474: for (i = 1; i < m; i++) {
475: v = aa + 49 * ai[i];
476: vi = aj + ai[i];
477: nz = diag[i] - ai[i];
478: s[0] = b[i2];
479: s[1] = b[i2 + 1];
480: s[2] = b[i2 + 2];
481: s[3] = b[i2 + 3];
482: s[4] = b[i2 + 4];
483: s[5] = b[i2 + 5];
484: s[6] = b[i2 + 6];
485: while (nz--) {
486: idx = 7 * (*vi++);
487: xw[0] = x[idx];
488: xw[1] = x[1 + idx];
489: xw[2] = x[2 + idx];
490: xw[3] = x[3 + idx];
491: xw[4] = x[4 + idx];
492: xw[5] = x[5 + idx];
493: xw[6] = x[6 + idx];
494: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
495: v += 49;
496: }
497: t[i2] = s[0];
498: t[i2 + 1] = s[1];
499: t[i2 + 2] = s[2];
500: t[i2 + 3] = s[3];
501: t[i2 + 4] = s[4];
502: t[i2 + 5] = s[5];
503: t[i2 + 6] = s[6];
504: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
505: x[i2] = xw[0];
506: x[i2 + 1] = xw[1];
507: x[i2 + 2] = xw[2];
508: x[i2 + 3] = xw[3];
509: x[i2 + 4] = xw[4];
510: x[i2 + 5] = xw[5];
511: x[i2 + 6] = xw[6];
512: idiag += 49;
513: i2 += 7;
514: }
515: break;
516: default:
517: PetscKernel_w_gets_Ar_times_v(bs, bs, b, idiag, x);
518: PetscCall(PetscArraycpy(t, b, bs));
519: i2 = bs;
520: idiag += bs2;
521: for (i = 1; i < m; i++) {
522: v = aa + bs2 * ai[i];
523: vi = aj + ai[i];
524: nz = diag[i] - ai[i];
526: PetscCall(PetscArraycpy(w, b + i2, bs));
527: /* copy all rows of x that are needed into contiguous space */
528: workt = work;
529: for (j = 0; j < nz; j++) {
530: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
531: workt += bs;
532: }
533: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
534: PetscCall(PetscArraycpy(t + i2, w, bs));
535: PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);
537: idiag += bs2;
538: i2 += bs;
539: }
540: break;
541: }
542: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
543: PetscCall(PetscLogFlops(1.0 * bs2 * a->nz));
544: xb = t;
545: } else xb = b;
546: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
547: idiag = a->idiag + bs2 * (a->mbs - 1);
548: i2 = bs * (m - 1);
549: switch (bs) {
550: case 1:
551: s[0] = xb[i2];
552: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
553: x[i2] = xw[0];
554: i2 -= 1;
555: for (i = m - 2; i >= 0; i--) {
556: v = aa + (diag[i] + 1);
557: vi = aj + diag[i] + 1;
558: nz = ai[i + 1] - diag[i] - 1;
559: s[0] = xb[i2];
560: for (j = 0; j < nz; j++) {
561: xw[0] = x[vi[j]];
562: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
563: }
564: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
565: x[i2] = xw[0];
566: idiag -= 1;
567: i2 -= 1;
568: }
569: break;
570: case 2:
571: s[0] = xb[i2];
572: s[1] = xb[i2 + 1];
573: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
574: x[i2] = xw[0];
575: x[i2 + 1] = xw[1];
576: i2 -= 2;
577: idiag -= 4;
578: for (i = m - 2; i >= 0; i--) {
579: v = aa + 4 * (diag[i] + 1);
580: vi = aj + diag[i] + 1;
581: nz = ai[i + 1] - diag[i] - 1;
582: s[0] = xb[i2];
583: s[1] = xb[i2 + 1];
584: for (j = 0; j < nz; j++) {
585: idx = 2 * vi[j];
586: it = 4 * j;
587: xw[0] = x[idx];
588: xw[1] = x[1 + idx];
589: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
590: }
591: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
592: x[i2] = xw[0];
593: x[i2 + 1] = xw[1];
594: idiag -= 4;
595: i2 -= 2;
596: }
597: break;
598: case 3:
599: s[0] = xb[i2];
600: s[1] = xb[i2 + 1];
601: s[2] = xb[i2 + 2];
602: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
603: x[i2] = xw[0];
604: x[i2 + 1] = xw[1];
605: x[i2 + 2] = xw[2];
606: i2 -= 3;
607: idiag -= 9;
608: for (i = m - 2; i >= 0; i--) {
609: v = aa + 9 * (diag[i] + 1);
610: vi = aj + diag[i] + 1;
611: nz = ai[i + 1] - diag[i] - 1;
612: s[0] = xb[i2];
613: s[1] = xb[i2 + 1];
614: s[2] = xb[i2 + 2];
615: while (nz--) {
616: idx = 3 * (*vi++);
617: xw[0] = x[idx];
618: xw[1] = x[1 + idx];
619: xw[2] = x[2 + idx];
620: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
621: v += 9;
622: }
623: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
624: x[i2] = xw[0];
625: x[i2 + 1] = xw[1];
626: x[i2 + 2] = xw[2];
627: idiag -= 9;
628: i2 -= 3;
629: }
630: break;
631: case 4:
632: s[0] = xb[i2];
633: s[1] = xb[i2 + 1];
634: s[2] = xb[i2 + 2];
635: s[3] = xb[i2 + 3];
636: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
637: x[i2] = xw[0];
638: x[i2 + 1] = xw[1];
639: x[i2 + 2] = xw[2];
640: x[i2 + 3] = xw[3];
641: i2 -= 4;
642: idiag -= 16;
643: for (i = m - 2; i >= 0; i--) {
644: v = aa + 16 * (diag[i] + 1);
645: vi = aj + diag[i] + 1;
646: nz = ai[i + 1] - diag[i] - 1;
647: s[0] = xb[i2];
648: s[1] = xb[i2 + 1];
649: s[2] = xb[i2 + 2];
650: s[3] = xb[i2 + 3];
651: while (nz--) {
652: idx = 4 * (*vi++);
653: xw[0] = x[idx];
654: xw[1] = x[1 + idx];
655: xw[2] = x[2 + idx];
656: xw[3] = x[3 + idx];
657: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
658: v += 16;
659: }
660: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
661: x[i2] = xw[0];
662: x[i2 + 1] = xw[1];
663: x[i2 + 2] = xw[2];
664: x[i2 + 3] = xw[3];
665: idiag -= 16;
666: i2 -= 4;
667: }
668: break;
669: case 5:
670: s[0] = xb[i2];
671: s[1] = xb[i2 + 1];
672: s[2] = xb[i2 + 2];
673: s[3] = xb[i2 + 3];
674: s[4] = xb[i2 + 4];
675: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
676: x[i2] = xw[0];
677: x[i2 + 1] = xw[1];
678: x[i2 + 2] = xw[2];
679: x[i2 + 3] = xw[3];
680: x[i2 + 4] = xw[4];
681: i2 -= 5;
682: idiag -= 25;
683: for (i = m - 2; i >= 0; i--) {
684: v = aa + 25 * (diag[i] + 1);
685: vi = aj + diag[i] + 1;
686: nz = ai[i + 1] - diag[i] - 1;
687: s[0] = xb[i2];
688: s[1] = xb[i2 + 1];
689: s[2] = xb[i2 + 2];
690: s[3] = xb[i2 + 3];
691: s[4] = xb[i2 + 4];
692: while (nz--) {
693: idx = 5 * (*vi++);
694: xw[0] = x[idx];
695: xw[1] = x[1 + idx];
696: xw[2] = x[2 + idx];
697: xw[3] = x[3 + idx];
698: xw[4] = x[4 + idx];
699: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
700: v += 25;
701: }
702: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
703: x[i2] = xw[0];
704: x[i2 + 1] = xw[1];
705: x[i2 + 2] = xw[2];
706: x[i2 + 3] = xw[3];
707: x[i2 + 4] = xw[4];
708: idiag -= 25;
709: i2 -= 5;
710: }
711: break;
712: case 6:
713: s[0] = xb[i2];
714: s[1] = xb[i2 + 1];
715: s[2] = xb[i2 + 2];
716: s[3] = xb[i2 + 3];
717: s[4] = xb[i2 + 4];
718: s[5] = xb[i2 + 5];
719: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
720: x[i2] = xw[0];
721: x[i2 + 1] = xw[1];
722: x[i2 + 2] = xw[2];
723: x[i2 + 3] = xw[3];
724: x[i2 + 4] = xw[4];
725: x[i2 + 5] = xw[5];
726: i2 -= 6;
727: idiag -= 36;
728: for (i = m - 2; i >= 0; i--) {
729: v = aa + 36 * (diag[i] + 1);
730: vi = aj + diag[i] + 1;
731: nz = ai[i + 1] - diag[i] - 1;
732: s[0] = xb[i2];
733: s[1] = xb[i2 + 1];
734: s[2] = xb[i2 + 2];
735: s[3] = xb[i2 + 3];
736: s[4] = xb[i2 + 4];
737: s[5] = xb[i2 + 5];
738: while (nz--) {
739: idx = 6 * (*vi++);
740: xw[0] = x[idx];
741: xw[1] = x[1 + idx];
742: xw[2] = x[2 + idx];
743: xw[3] = x[3 + idx];
744: xw[4] = x[4 + idx];
745: xw[5] = x[5 + idx];
746: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
747: v += 36;
748: }
749: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
750: x[i2] = xw[0];
751: x[i2 + 1] = xw[1];
752: x[i2 + 2] = xw[2];
753: x[i2 + 3] = xw[3];
754: x[i2 + 4] = xw[4];
755: x[i2 + 5] = xw[5];
756: idiag -= 36;
757: i2 -= 6;
758: }
759: break;
760: case 7:
761: s[0] = xb[i2];
762: s[1] = xb[i2 + 1];
763: s[2] = xb[i2 + 2];
764: s[3] = xb[i2 + 3];
765: s[4] = xb[i2 + 4];
766: s[5] = xb[i2 + 5];
767: s[6] = xb[i2 + 6];
768: PetscKernel_v_gets_A_times_w_7(x, idiag, b);
769: x[i2] = xw[0];
770: x[i2 + 1] = xw[1];
771: x[i2 + 2] = xw[2];
772: x[i2 + 3] = xw[3];
773: x[i2 + 4] = xw[4];
774: x[i2 + 5] = xw[5];
775: x[i2 + 6] = xw[6];
776: i2 -= 7;
777: idiag -= 49;
778: for (i = m - 2; i >= 0; i--) {
779: v = aa + 49 * (diag[i] + 1);
780: vi = aj + diag[i] + 1;
781: nz = ai[i + 1] - diag[i] - 1;
782: s[0] = xb[i2];
783: s[1] = xb[i2 + 1];
784: s[2] = xb[i2 + 2];
785: s[3] = xb[i2 + 3];
786: s[4] = xb[i2 + 4];
787: s[5] = xb[i2 + 5];
788: s[6] = xb[i2 + 6];
789: while (nz--) {
790: idx = 7 * (*vi++);
791: xw[0] = x[idx];
792: xw[1] = x[1 + idx];
793: xw[2] = x[2 + idx];
794: xw[3] = x[3 + idx];
795: xw[4] = x[4 + idx];
796: xw[5] = x[5 + idx];
797: xw[6] = x[6 + idx];
798: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
799: v += 49;
800: }
801: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
802: x[i2] = xw[0];
803: x[i2 + 1] = xw[1];
804: x[i2 + 2] = xw[2];
805: x[i2 + 3] = xw[3];
806: x[i2 + 4] = xw[4];
807: x[i2 + 5] = xw[5];
808: x[i2 + 6] = xw[6];
809: idiag -= 49;
810: i2 -= 7;
811: }
812: break;
813: default:
814: PetscCall(PetscArraycpy(w, xb + i2, bs));
815: PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);
816: i2 -= bs;
817: idiag -= bs2;
818: for (i = m - 2; i >= 0; i--) {
819: v = aa + bs2 * (diag[i] + 1);
820: vi = aj + diag[i] + 1;
821: nz = ai[i + 1] - diag[i] - 1;
823: PetscCall(PetscArraycpy(w, xb + i2, bs));
824: /* copy all rows of x that are needed into contiguous space */
825: workt = work;
826: for (j = 0; j < nz; j++) {
827: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
828: workt += bs;
829: }
830: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
831: PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);
833: idiag -= bs2;
834: i2 -= bs;
835: }
836: break;
837: }
838: PetscCall(PetscLogFlops(1.0 * bs2 * (a->nz)));
839: }
840: its--;
841: }
842: while (its--) {
843: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
844: idiag = a->idiag;
845: i2 = 0;
846: switch (bs) {
847: case 1:
848: for (i = 0; i < m; i++) {
849: v = aa + ai[i];
850: vi = aj + ai[i];
851: nz = ai[i + 1] - ai[i];
852: s[0] = b[i2];
853: for (j = 0; j < nz; j++) {
854: xw[0] = x[vi[j]];
855: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
856: }
857: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
858: x[i2] += xw[0];
859: idiag += 1;
860: i2 += 1;
861: }
862: break;
863: case 2:
864: for (i = 0; i < m; i++) {
865: v = aa + 4 * ai[i];
866: vi = aj + ai[i];
867: nz = ai[i + 1] - ai[i];
868: s[0] = b[i2];
869: s[1] = b[i2 + 1];
870: for (j = 0; j < nz; j++) {
871: idx = 2 * vi[j];
872: it = 4 * j;
873: xw[0] = x[idx];
874: xw[1] = x[1 + idx];
875: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
876: }
877: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
878: x[i2] += xw[0];
879: x[i2 + 1] += xw[1];
880: idiag += 4;
881: i2 += 2;
882: }
883: break;
884: case 3:
885: for (i = 0; i < m; i++) {
886: v = aa + 9 * ai[i];
887: vi = aj + ai[i];
888: nz = ai[i + 1] - ai[i];
889: s[0] = b[i2];
890: s[1] = b[i2 + 1];
891: s[2] = b[i2 + 2];
892: while (nz--) {
893: idx = 3 * (*vi++);
894: xw[0] = x[idx];
895: xw[1] = x[1 + idx];
896: xw[2] = x[2 + idx];
897: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
898: v += 9;
899: }
900: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
901: x[i2] += xw[0];
902: x[i2 + 1] += xw[1];
903: x[i2 + 2] += xw[2];
904: idiag += 9;
905: i2 += 3;
906: }
907: break;
908: case 4:
909: for (i = 0; i < m; i++) {
910: v = aa + 16 * ai[i];
911: vi = aj + ai[i];
912: nz = ai[i + 1] - ai[i];
913: s[0] = b[i2];
914: s[1] = b[i2 + 1];
915: s[2] = b[i2 + 2];
916: s[3] = b[i2 + 3];
917: while (nz--) {
918: idx = 4 * (*vi++);
919: xw[0] = x[idx];
920: xw[1] = x[1 + idx];
921: xw[2] = x[2 + idx];
922: xw[3] = x[3 + idx];
923: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
924: v += 16;
925: }
926: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
927: x[i2] += xw[0];
928: x[i2 + 1] += xw[1];
929: x[i2 + 2] += xw[2];
930: x[i2 + 3] += xw[3];
931: idiag += 16;
932: i2 += 4;
933: }
934: break;
935: case 5:
936: for (i = 0; i < m; i++) {
937: v = aa + 25 * ai[i];
938: vi = aj + ai[i];
939: nz = ai[i + 1] - ai[i];
940: s[0] = b[i2];
941: s[1] = b[i2 + 1];
942: s[2] = b[i2 + 2];
943: s[3] = b[i2 + 3];
944: s[4] = b[i2 + 4];
945: while (nz--) {
946: idx = 5 * (*vi++);
947: xw[0] = x[idx];
948: xw[1] = x[1 + idx];
949: xw[2] = x[2 + idx];
950: xw[3] = x[3 + idx];
951: xw[4] = x[4 + idx];
952: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
953: v += 25;
954: }
955: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
956: x[i2] += xw[0];
957: x[i2 + 1] += xw[1];
958: x[i2 + 2] += xw[2];
959: x[i2 + 3] += xw[3];
960: x[i2 + 4] += xw[4];
961: idiag += 25;
962: i2 += 5;
963: }
964: break;
965: case 6:
966: for (i = 0; i < m; i++) {
967: v = aa + 36 * ai[i];
968: vi = aj + ai[i];
969: nz = ai[i + 1] - ai[i];
970: s[0] = b[i2];
971: s[1] = b[i2 + 1];
972: s[2] = b[i2 + 2];
973: s[3] = b[i2 + 3];
974: s[4] = b[i2 + 4];
975: s[5] = b[i2 + 5];
976: while (nz--) {
977: idx = 6 * (*vi++);
978: xw[0] = x[idx];
979: xw[1] = x[1 + idx];
980: xw[2] = x[2 + idx];
981: xw[3] = x[3 + idx];
982: xw[4] = x[4 + idx];
983: xw[5] = x[5 + idx];
984: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
985: v += 36;
986: }
987: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
988: x[i2] += xw[0];
989: x[i2 + 1] += xw[1];
990: x[i2 + 2] += xw[2];
991: x[i2 + 3] += xw[3];
992: x[i2 + 4] += xw[4];
993: x[i2 + 5] += xw[5];
994: idiag += 36;
995: i2 += 6;
996: }
997: break;
998: case 7:
999: for (i = 0; i < m; i++) {
1000: v = aa + 49 * ai[i];
1001: vi = aj + ai[i];
1002: nz = ai[i + 1] - ai[i];
1003: s[0] = b[i2];
1004: s[1] = b[i2 + 1];
1005: s[2] = b[i2 + 2];
1006: s[3] = b[i2 + 3];
1007: s[4] = b[i2 + 4];
1008: s[5] = b[i2 + 5];
1009: s[6] = b[i2 + 6];
1010: while (nz--) {
1011: idx = 7 * (*vi++);
1012: xw[0] = x[idx];
1013: xw[1] = x[1 + idx];
1014: xw[2] = x[2 + idx];
1015: xw[3] = x[3 + idx];
1016: xw[4] = x[4 + idx];
1017: xw[5] = x[5 + idx];
1018: xw[6] = x[6 + idx];
1019: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
1020: v += 49;
1021: }
1022: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
1023: x[i2] += xw[0];
1024: x[i2 + 1] += xw[1];
1025: x[i2 + 2] += xw[2];
1026: x[i2 + 3] += xw[3];
1027: x[i2 + 4] += xw[4];
1028: x[i2 + 5] += xw[5];
1029: x[i2 + 6] += xw[6];
1030: idiag += 49;
1031: i2 += 7;
1032: }
1033: break;
1034: default:
1035: for (i = 0; i < m; i++) {
1036: v = aa + bs2 * ai[i];
1037: vi = aj + ai[i];
1038: nz = ai[i + 1] - ai[i];
1040: PetscCall(PetscArraycpy(w, b + i2, bs));
1041: /* copy all rows of x that are needed into contiguous space */
1042: workt = work;
1043: for (j = 0; j < nz; j++) {
1044: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
1045: workt += bs;
1046: }
1047: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
1048: PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2);
1050: idiag += bs2;
1051: i2 += bs;
1052: }
1053: break;
1054: }
1055: PetscCall(PetscLogFlops(2.0 * bs2 * a->nz));
1056: }
1057: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1058: idiag = a->idiag + bs2 * (a->mbs - 1);
1059: i2 = bs * (m - 1);
1060: switch (bs) {
1061: case 1:
1062: for (i = m - 1; i >= 0; i--) {
1063: v = aa + ai[i];
1064: vi = aj + ai[i];
1065: nz = ai[i + 1] - ai[i];
1066: s[0] = b[i2];
1067: for (j = 0; j < nz; j++) {
1068: xw[0] = x[vi[j]];
1069: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
1070: }
1071: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
1072: x[i2] += xw[0];
1073: idiag -= 1;
1074: i2 -= 1;
1075: }
1076: break;
1077: case 2:
1078: for (i = m - 1; i >= 0; i--) {
1079: v = aa + 4 * ai[i];
1080: vi = aj + ai[i];
1081: nz = ai[i + 1] - ai[i];
1082: s[0] = b[i2];
1083: s[1] = b[i2 + 1];
1084: for (j = 0; j < nz; j++) {
1085: idx = 2 * vi[j];
1086: it = 4 * j;
1087: xw[0] = x[idx];
1088: xw[1] = x[1 + idx];
1089: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
1090: }
1091: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
1092: x[i2] += xw[0];
1093: x[i2 + 1] += xw[1];
1094: idiag -= 4;
1095: i2 -= 2;
1096: }
1097: break;
1098: case 3:
1099: for (i = m - 1; i >= 0; i--) {
1100: v = aa + 9 * ai[i];
1101: vi = aj + ai[i];
1102: nz = ai[i + 1] - ai[i];
1103: s[0] = b[i2];
1104: s[1] = b[i2 + 1];
1105: s[2] = b[i2 + 2];
1106: while (nz--) {
1107: idx = 3 * (*vi++);
1108: xw[0] = x[idx];
1109: xw[1] = x[1 + idx];
1110: xw[2] = x[2 + idx];
1111: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
1112: v += 9;
1113: }
1114: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
1115: x[i2] += xw[0];
1116: x[i2 + 1] += xw[1];
1117: x[i2 + 2] += xw[2];
1118: idiag -= 9;
1119: i2 -= 3;
1120: }
1121: break;
1122: case 4:
1123: for (i = m - 1; i >= 0; i--) {
1124: v = aa + 16 * ai[i];
1125: vi = aj + ai[i];
1126: nz = ai[i + 1] - ai[i];
1127: s[0] = b[i2];
1128: s[1] = b[i2 + 1];
1129: s[2] = b[i2 + 2];
1130: s[3] = b[i2 + 3];
1131: while (nz--) {
1132: idx = 4 * (*vi++);
1133: xw[0] = x[idx];
1134: xw[1] = x[1 + idx];
1135: xw[2] = x[2 + idx];
1136: xw[3] = x[3 + idx];
1137: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
1138: v += 16;
1139: }
1140: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
1141: x[i2] += xw[0];
1142: x[i2 + 1] += xw[1];
1143: x[i2 + 2] += xw[2];
1144: x[i2 + 3] += xw[3];
1145: idiag -= 16;
1146: i2 -= 4;
1147: }
1148: break;
1149: case 5:
1150: for (i = m - 1; i >= 0; i--) {
1151: v = aa + 25 * ai[i];
1152: vi = aj + ai[i];
1153: nz = ai[i + 1] - ai[i];
1154: s[0] = b[i2];
1155: s[1] = b[i2 + 1];
1156: s[2] = b[i2 + 2];
1157: s[3] = b[i2 + 3];
1158: s[4] = b[i2 + 4];
1159: while (nz--) {
1160: idx = 5 * (*vi++);
1161: xw[0] = x[idx];
1162: xw[1] = x[1 + idx];
1163: xw[2] = x[2 + idx];
1164: xw[3] = x[3 + idx];
1165: xw[4] = x[4 + idx];
1166: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
1167: v += 25;
1168: }
1169: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
1170: x[i2] += xw[0];
1171: x[i2 + 1] += xw[1];
1172: x[i2 + 2] += xw[2];
1173: x[i2 + 3] += xw[3];
1174: x[i2 + 4] += xw[4];
1175: idiag -= 25;
1176: i2 -= 5;
1177: }
1178: break;
1179: case 6:
1180: for (i = m - 1; i >= 0; i--) {
1181: v = aa + 36 * ai[i];
1182: vi = aj + ai[i];
1183: nz = ai[i + 1] - ai[i];
1184: s[0] = b[i2];
1185: s[1] = b[i2 + 1];
1186: s[2] = b[i2 + 2];
1187: s[3] = b[i2 + 3];
1188: s[4] = b[i2 + 4];
1189: s[5] = b[i2 + 5];
1190: while (nz--) {
1191: idx = 6 * (*vi++);
1192: xw[0] = x[idx];
1193: xw[1] = x[1 + idx];
1194: xw[2] = x[2 + idx];
1195: xw[3] = x[3 + idx];
1196: xw[4] = x[4 + idx];
1197: xw[5] = x[5 + idx];
1198: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
1199: v += 36;
1200: }
1201: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
1202: x[i2] += xw[0];
1203: x[i2 + 1] += xw[1];
1204: x[i2 + 2] += xw[2];
1205: x[i2 + 3] += xw[3];
1206: x[i2 + 4] += xw[4];
1207: x[i2 + 5] += xw[5];
1208: idiag -= 36;
1209: i2 -= 6;
1210: }
1211: break;
1212: case 7:
1213: for (i = m - 1; i >= 0; i--) {
1214: v = aa + 49 * ai[i];
1215: vi = aj + ai[i];
1216: nz = ai[i + 1] - ai[i];
1217: s[0] = b[i2];
1218: s[1] = b[i2 + 1];
1219: s[2] = b[i2 + 2];
1220: s[3] = b[i2 + 3];
1221: s[4] = b[i2 + 4];
1222: s[5] = b[i2 + 5];
1223: s[6] = b[i2 + 6];
1224: while (nz--) {
1225: idx = 7 * (*vi++);
1226: xw[0] = x[idx];
1227: xw[1] = x[1 + idx];
1228: xw[2] = x[2 + idx];
1229: xw[3] = x[3 + idx];
1230: xw[4] = x[4 + idx];
1231: xw[5] = x[5 + idx];
1232: xw[6] = x[6 + idx];
1233: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
1234: v += 49;
1235: }
1236: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
1237: x[i2] += xw[0];
1238: x[i2 + 1] += xw[1];
1239: x[i2 + 2] += xw[2];
1240: x[i2 + 3] += xw[3];
1241: x[i2 + 4] += xw[4];
1242: x[i2 + 5] += xw[5];
1243: x[i2 + 6] += xw[6];
1244: idiag -= 49;
1245: i2 -= 7;
1246: }
1247: break;
1248: default:
1249: for (i = m - 1; i >= 0; i--) {
1250: v = aa + bs2 * ai[i];
1251: vi = aj + ai[i];
1252: nz = ai[i + 1] - ai[i];
1254: PetscCall(PetscArraycpy(w, b + i2, bs));
1255: /* copy all rows of x that are needed into contiguous space */
1256: workt = work;
1257: for (j = 0; j < nz; j++) {
1258: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
1259: workt += bs;
1260: }
1261: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
1262: PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2);
1264: idiag -= bs2;
1265: i2 -= bs;
1266: }
1267: break;
1268: }
1269: PetscCall(PetscLogFlops(2.0 * bs2 * (a->nz)));
1270: }
1271: }
1272: PetscCall(VecRestoreArray(xx, &x));
1273: PetscCall(VecRestoreArrayRead(bb, &b));
1274: PetscFunctionReturn(PETSC_SUCCESS);
1275: }
1277: /*
1278: Special version for direct calls from Fortran (Used in PETSc-fun3d)
1279: */
1280: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1281: #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4
1282: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1283: #define matsetvaluesblocked4_ matsetvaluesblocked4
1284: #endif
1286: PETSC_EXTERN void matsetvaluesblocked4_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[])
1287: {
1288: Mat A = *AA;
1289: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1290: PetscInt *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, N, m = *mm, n = *nn;
1291: PetscInt *ai = a->i, *ailen = a->ilen;
1292: PetscInt *aj = a->j, stepval, lastcol = -1;
1293: const PetscScalar *value = v;
1294: MatScalar *ap, *aa = a->a, *bap;
1296: PetscFunctionBegin;
1297: if (A->rmap->bs != 4) SETERRABORT(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Can only be called with a block size of 4");
1298: stepval = (n - 1) * 4;
1299: for (k = 0; k < m; k++) { /* loop over added rows */
1300: row = im[k];
1301: rp = aj + ai[row];
1302: ap = aa + 16 * ai[row];
1303: nrow = ailen[row];
1304: low = 0;
1305: high = nrow;
1306: for (l = 0; l < n; l++) { /* loop over added columns */
1307: col = in[l];
1308: if (col <= lastcol) low = 0;
1309: else high = nrow;
1310: lastcol = col;
1311: value = v + k * (stepval + 4 + l) * 4;
1312: while (high - low > 7) {
1313: t = (low + high) / 2;
1314: if (rp[t] > col) high = t;
1315: else low = t;
1316: }
1317: for (i = low; i < high; i++) {
1318: if (rp[i] > col) break;
1319: if (rp[i] == col) {
1320: bap = ap + 16 * i;
1321: for (ii = 0; ii < 4; ii++, value += stepval) {
1322: for (jj = ii; jj < 16; jj += 4) bap[jj] += *value++;
1323: }
1324: goto noinsert2;
1325: }
1326: }
1327: N = nrow++ - 1;
1328: high++; /* added new column index thus must search to one higher than before */
1329: /* shift up all the later entries in this row */
1330: for (ii = N; ii >= i; ii--) {
1331: rp[ii + 1] = rp[ii];
1332: PetscCallVoid(PetscArraycpy(ap + 16 * (ii + 1), ap + 16 * (ii), 16));
1333: }
1334: if (N >= i) PetscCallVoid(PetscArrayzero(ap + 16 * i, 16));
1335: rp[i] = col;
1336: bap = ap + 16 * i;
1337: for (ii = 0; ii < 4; ii++, value += stepval) {
1338: for (jj = ii; jj < 16; jj += 4) bap[jj] = *value++;
1339: }
1340: noinsert2:;
1341: low = i;
1342: }
1343: ailen[row] = nrow;
1344: }
1345: PetscFunctionReturnVoid();
1346: }
1348: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1349: #define matsetvalues4_ MATSETVALUES4
1350: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1351: #define matsetvalues4_ matsetvalues4
1352: #endif
1354: PETSC_EXTERN void matsetvalues4_(Mat *AA, PetscInt *mm, PetscInt *im, PetscInt *nn, PetscInt *in, PetscScalar *v)
1355: {
1356: Mat A = *AA;
1357: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1358: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, N, n = *nn, m = *mm;
1359: PetscInt *ai = a->i, *ailen = a->ilen;
1360: PetscInt *aj = a->j, brow, bcol;
1361: PetscInt ridx, cidx, lastcol = -1;
1362: MatScalar *ap, value, *aa = a->a, *bap;
1364: PetscFunctionBegin;
1365: for (k = 0; k < m; k++) { /* loop over added rows */
1366: row = im[k];
1367: brow = row / 4;
1368: rp = aj + ai[brow];
1369: ap = aa + 16 * ai[brow];
1370: nrow = ailen[brow];
1371: low = 0;
1372: high = nrow;
1373: for (l = 0; l < n; l++) { /* loop over added columns */
1374: col = in[l];
1375: bcol = col / 4;
1376: ridx = row % 4;
1377: cidx = col % 4;
1378: value = v[l + k * n];
1379: if (col <= lastcol) low = 0;
1380: else high = nrow;
1381: lastcol = col;
1382: while (high - low > 7) {
1383: t = (low + high) / 2;
1384: if (rp[t] > bcol) high = t;
1385: else low = t;
1386: }
1387: for (i = low; i < high; i++) {
1388: if (rp[i] > bcol) break;
1389: if (rp[i] == bcol) {
1390: bap = ap + 16 * i + 4 * cidx + ridx;
1391: *bap += value;
1392: goto noinsert1;
1393: }
1394: }
1395: N = nrow++ - 1;
1396: high++; /* added new column thus must search to one higher than before */
1397: /* shift up all the later entries in this row */
1398: PetscCallVoid(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
1399: PetscCallVoid(PetscArraymove(ap + 16 * i + 16, ap + 16 * i, 16 * (N - i + 1)));
1400: PetscCallVoid(PetscArrayzero(ap + 16 * i, 16));
1401: rp[i] = bcol;
1402: ap[16 * i + 4 * cidx + ridx] = value;
1403: noinsert1:;
1404: low = i;
1405: }
1406: ailen[brow] = nrow;
1407: }
1408: PetscFunctionReturnVoid();
1409: }
1411: /*
1412: Checks for missing diagonals
1413: */
1414: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A, PetscBool *missing, PetscInt *d)
1415: {
1416: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1417: PetscInt *diag, *ii = a->i, i;
1419: PetscFunctionBegin;
1420: PetscCall(MatMarkDiagonal_SeqBAIJ(A));
1421: *missing = PETSC_FALSE;
1422: if (A->rmap->n > 0 && !ii) {
1423: *missing = PETSC_TRUE;
1424: if (d) *d = 0;
1425: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1426: } else {
1427: PetscInt n;
1428: n = PetscMin(a->mbs, a->nbs);
1429: diag = a->diag;
1430: for (i = 0; i < n; i++) {
1431: if (diag[i] >= ii[i + 1]) {
1432: *missing = PETSC_TRUE;
1433: if (d) *d = i;
1434: PetscCall(PetscInfo(A, "Matrix is missing block diagonal number %" PetscInt_FMT "\n", i));
1435: break;
1436: }
1437: }
1438: }
1439: PetscFunctionReturn(PETSC_SUCCESS);
1440: }
1442: PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A)
1443: {
1444: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1445: PetscInt i, j, m = a->mbs;
1447: PetscFunctionBegin;
1448: if (!a->diag) {
1449: PetscCall(PetscMalloc1(m, &a->diag));
1450: a->free_diag = PETSC_TRUE;
1451: }
1452: for (i = 0; i < m; i++) {
1453: a->diag[i] = a->i[i + 1];
1454: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1455: if (a->j[j] == i) {
1456: a->diag[i] = j;
1457: break;
1458: }
1459: }
1460: }
1461: PetscFunctionReturn(PETSC_SUCCESS);
1462: }
1464: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *inia[], const PetscInt *inja[], PetscBool *done)
1465: {
1466: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1467: PetscInt i, j, n = a->mbs, nz = a->i[n], *tia, *tja, bs = A->rmap->bs, k, l, cnt;
1468: PetscInt **ia = (PetscInt **)inia, **ja = (PetscInt **)inja;
1470: PetscFunctionBegin;
1471: *nn = n;
1472: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
1473: if (symmetric) {
1474: PetscCall(MatToSymmetricIJ_SeqAIJ(n, a->i, a->j, PETSC_TRUE, 0, 0, &tia, &tja));
1475: nz = tia[n];
1476: } else {
1477: tia = a->i;
1478: tja = a->j;
1479: }
1481: if (!blockcompressed && bs > 1) {
1482: (*nn) *= bs;
1483: /* malloc & create the natural set of indices */
1484: PetscCall(PetscMalloc1((n + 1) * bs, ia));
1485: if (n) {
1486: (*ia)[0] = oshift;
1487: for (j = 1; j < bs; j++) (*ia)[j] = (tia[1] - tia[0]) * bs + (*ia)[j - 1];
1488: }
1490: for (i = 1; i < n; i++) {
1491: (*ia)[i * bs] = (tia[i] - tia[i - 1]) * bs + (*ia)[i * bs - 1];
1492: for (j = 1; j < bs; j++) (*ia)[i * bs + j] = (tia[i + 1] - tia[i]) * bs + (*ia)[i * bs + j - 1];
1493: }
1494: if (n) (*ia)[n * bs] = (tia[n] - tia[n - 1]) * bs + (*ia)[n * bs - 1];
1496: if (inja) {
1497: PetscCall(PetscMalloc1(nz * bs * bs, ja));
1498: cnt = 0;
1499: for (i = 0; i < n; i++) {
1500: for (j = 0; j < bs; j++) {
1501: for (k = tia[i]; k < tia[i + 1]; k++) {
1502: for (l = 0; l < bs; l++) (*ja)[cnt++] = bs * tja[k] + l;
1503: }
1504: }
1505: }
1506: }
1508: if (symmetric) { /* deallocate memory allocated in MatToSymmetricIJ_SeqAIJ() */
1509: PetscCall(PetscFree(tia));
1510: PetscCall(PetscFree(tja));
1511: }
1512: } else if (oshift == 1) {
1513: if (symmetric) {
1514: nz = tia[A->rmap->n / bs];
1515: /* add 1 to i and j indices */
1516: for (i = 0; i < A->rmap->n / bs + 1; i++) tia[i] = tia[i] + 1;
1517: *ia = tia;
1518: if (ja) {
1519: for (i = 0; i < nz; i++) tja[i] = tja[i] + 1;
1520: *ja = tja;
1521: }
1522: } else {
1523: nz = a->i[A->rmap->n / bs];
1524: /* malloc space and add 1 to i and j indices */
1525: PetscCall(PetscMalloc1(A->rmap->n / bs + 1, ia));
1526: for (i = 0; i < A->rmap->n / bs + 1; i++) (*ia)[i] = a->i[i] + 1;
1527: if (ja) {
1528: PetscCall(PetscMalloc1(nz, ja));
1529: for (i = 0; i < nz; i++) (*ja)[i] = a->j[i] + 1;
1530: }
1531: }
1532: } else {
1533: *ia = tia;
1534: if (ja) *ja = tja;
1535: }
1536: PetscFunctionReturn(PETSC_SUCCESS);
1537: }
1539: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
1540: {
1541: PetscFunctionBegin;
1542: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
1543: if ((!blockcompressed && A->rmap->bs > 1) || (symmetric || oshift == 1)) {
1544: PetscCall(PetscFree(*ia));
1545: if (ja) PetscCall(PetscFree(*ja));
1546: }
1547: PetscFunctionReturn(PETSC_SUCCESS);
1548: }
1550: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
1551: {
1552: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1554: PetscFunctionBegin;
1555: if (A->hash_active) {
1556: PetscInt bs;
1557: A->ops[0] = a->cops;
1558: PetscCall(PetscHMapIJVDestroy(&a->ht));
1559: PetscCall(MatGetBlockSize(A, &bs));
1560: if (bs > 1) PetscCall(PetscHSetIJDestroy(&a->bht));
1561: PetscCall(PetscFree(a->dnz));
1562: PetscCall(PetscFree(a->bdnz));
1563: A->hash_active = PETSC_FALSE;
1564: }
1565: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->N, A->cmap->n, a->nz));
1566: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1567: PetscCall(ISDestroy(&a->row));
1568: PetscCall(ISDestroy(&a->col));
1569: if (a->free_diag) PetscCall(PetscFree(a->diag));
1570: PetscCall(PetscFree(a->idiag));
1571: if (a->free_imax_ilen) PetscCall(PetscFree2(a->imax, a->ilen));
1572: PetscCall(PetscFree(a->solve_work));
1573: PetscCall(PetscFree(a->mult_work));
1574: PetscCall(PetscFree(a->sor_workt));
1575: PetscCall(PetscFree(a->sor_work));
1576: PetscCall(ISDestroy(&a->icol));
1577: PetscCall(PetscFree(a->saved_values));
1578: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1580: PetscCall(MatDestroy(&a->sbaijMat));
1581: PetscCall(MatDestroy(&a->parent));
1582: PetscCall(PetscFree(A->data));
1584: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1585: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJGetArray_C", NULL));
1586: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJRestoreArray_C", NULL));
1587: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1588: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1589: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetColumnIndices_C", NULL));
1590: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqaij_C", NULL));
1591: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqsbaij_C", NULL));
1592: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocation_C", NULL));
1593: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocationCSR_C", NULL));
1594: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqbstrm_C", NULL));
1595: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1596: #if defined(PETSC_HAVE_HYPRE)
1597: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_hypre_C", NULL));
1598: #endif
1599: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_is_C", NULL));
1600: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1601: PetscFunctionReturn(PETSC_SUCCESS);
1602: }
1604: static PetscErrorCode MatSetOption_SeqBAIJ(Mat A, MatOption op, PetscBool flg)
1605: {
1606: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1608: PetscFunctionBegin;
1609: switch (op) {
1610: case MAT_ROW_ORIENTED:
1611: a->roworiented = flg;
1612: break;
1613: case MAT_KEEP_NONZERO_PATTERN:
1614: a->keepnonzeropattern = flg;
1615: break;
1616: case MAT_NEW_NONZERO_LOCATIONS:
1617: a->nonew = (flg ? 0 : 1);
1618: break;
1619: case MAT_NEW_NONZERO_LOCATION_ERR:
1620: a->nonew = (flg ? -1 : 0);
1621: break;
1622: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1623: a->nonew = (flg ? -2 : 0);
1624: break;
1625: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1626: a->nounused = (flg ? -1 : 0);
1627: break;
1628: default:
1629: break;
1630: }
1631: PetscFunctionReturn(PETSC_SUCCESS);
1632: }
1634: /* used for both SeqBAIJ and SeqSBAIJ matrices */
1635: PetscErrorCode MatGetRow_SeqBAIJ_private(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v, PetscInt *ai, PetscInt *aj, PetscScalar *aa)
1636: {
1637: PetscInt itmp, i, j, k, M, bn, bp, *idx_i, bs, bs2;
1638: MatScalar *aa_i;
1639: PetscScalar *v_i;
1641: PetscFunctionBegin;
1642: bs = A->rmap->bs;
1643: bs2 = bs * bs;
1644: PetscCheck(row >= 0 && row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range", row);
1646: bn = row / bs; /* Block number */
1647: bp = row % bs; /* Block Position */
1648: M = ai[bn + 1] - ai[bn];
1649: *nz = bs * M;
1651: if (v) {
1652: *v = NULL;
1653: if (*nz) {
1654: PetscCall(PetscMalloc1(*nz, v));
1655: for (i = 0; i < M; i++) { /* for each block in the block row */
1656: v_i = *v + i * bs;
1657: aa_i = aa + bs2 * (ai[bn] + i);
1658: for (j = bp, k = 0; j < bs2; j += bs, k++) v_i[k] = aa_i[j];
1659: }
1660: }
1661: }
1663: if (idx) {
1664: *idx = NULL;
1665: if (*nz) {
1666: PetscCall(PetscMalloc1(*nz, idx));
1667: for (i = 0; i < M; i++) { /* for each block in the block row */
1668: idx_i = *idx + i * bs;
1669: itmp = bs * aj[ai[bn] + i];
1670: for (j = 0; j < bs; j++) idx_i[j] = itmp++;
1671: }
1672: }
1673: }
1674: PetscFunctionReturn(PETSC_SUCCESS);
1675: }
1677: PetscErrorCode MatGetRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1678: {
1679: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1681: PetscFunctionBegin;
1682: PetscCall(MatGetRow_SeqBAIJ_private(A, row, nz, idx, v, a->i, a->j, a->a));
1683: PetscFunctionReturn(PETSC_SUCCESS);
1684: }
1686: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1687: {
1688: PetscFunctionBegin;
1689: if (idx) PetscCall(PetscFree(*idx));
1690: if (v) PetscCall(PetscFree(*v));
1691: PetscFunctionReturn(PETSC_SUCCESS);
1692: }
1694: static PetscErrorCode MatTranspose_SeqBAIJ(Mat A, MatReuse reuse, Mat *B)
1695: {
1696: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *at;
1697: Mat C;
1698: PetscInt i, j, k, *aj = a->j, *ai = a->i, bs = A->rmap->bs, mbs = a->mbs, nbs = a->nbs, *atfill;
1699: PetscInt bs2 = a->bs2, *ati, *atj, anzj, kr;
1700: MatScalar *ata, *aa = a->a;
1702: PetscFunctionBegin;
1703: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *B));
1704: PetscCall(PetscCalloc1(1 + nbs, &atfill));
1705: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1706: for (i = 0; i < ai[mbs]; i++) atfill[aj[i]] += 1; /* count num of non-zeros in row aj[i] */
1708: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
1709: PetscCall(MatSetSizes(C, A->cmap->n, A->rmap->N, A->cmap->n, A->rmap->N));
1710: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
1711: PetscCall(MatSeqBAIJSetPreallocation(C, bs, 0, atfill));
1713: at = (Mat_SeqBAIJ *)C->data;
1714: ati = at->i;
1715: for (i = 0; i < nbs; i++) at->ilen[i] = at->imax[i] = ati[i + 1] - ati[i];
1716: } else {
1717: C = *B;
1718: at = (Mat_SeqBAIJ *)C->data;
1719: ati = at->i;
1720: }
1722: atj = at->j;
1723: ata = at->a;
1725: /* Copy ati into atfill so we have locations of the next free space in atj */
1726: PetscCall(PetscArraycpy(atfill, ati, nbs));
1728: /* Walk through A row-wise and mark nonzero entries of A^T. */
1729: for (i = 0; i < mbs; i++) {
1730: anzj = ai[i + 1] - ai[i];
1731: for (j = 0; j < anzj; j++) {
1732: atj[atfill[*aj]] = i;
1733: for (kr = 0; kr < bs; kr++) {
1734: for (k = 0; k < bs; k++) ata[bs2 * atfill[*aj] + k * bs + kr] = *aa++;
1735: }
1736: atfill[*aj++] += 1;
1737: }
1738: }
1739: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1740: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1742: /* Clean up temporary space and complete requests. */
1743: PetscCall(PetscFree(atfill));
1745: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1746: PetscCall(MatSetBlockSizes(C, A->cmap->bs, A->rmap->bs));
1747: *B = C;
1748: } else {
1749: PetscCall(MatHeaderMerge(A, &C));
1750: }
1751: PetscFunctionReturn(PETSC_SUCCESS);
1752: }
1754: static PetscErrorCode MatCompare_SeqBAIJ_Private(Mat A, Mat B, PetscReal tol, PetscBool *flg)
1755: {
1756: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *b = (Mat_SeqBAIJ *)B->data;
1758: PetscFunctionBegin;
1759: /* If the matrix/block dimensions are not equal, or no of nonzeros or shift */
1760: if (A->rmap->N != B->rmap->N || A->cmap->n != B->cmap->n || A->rmap->bs != B->rmap->bs || a->nz != b->nz) {
1761: *flg = PETSC_FALSE;
1762: PetscFunctionReturn(PETSC_SUCCESS);
1763: }
1765: /* if the a->i are the same */
1766: PetscCall(PetscArraycmp(a->i, b->i, a->mbs + 1, flg));
1767: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
1769: /* if a->j are the same */
1770: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
1771: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
1773: if (tol == 0.0) PetscCall(PetscArraycmp(a->a, b->a, a->nz * A->rmap->bs * A->rmap->bs, flg)); /* if a->a are the same */
1774: else {
1775: *flg = PETSC_TRUE;
1776: for (PetscInt i = 0; (i < a->nz * A->rmap->bs * A->rmap->bs) && *flg; ++i)
1777: if (PetscAbsScalar(a->a[i] - b->a[i]) > tol) *flg = PETSC_FALSE;
1778: }
1779: PetscFunctionReturn(PETSC_SUCCESS);
1780: }
1782: static PetscErrorCode MatIsTranspose_SeqBAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
1783: {
1784: Mat Btrans;
1786: PetscFunctionBegin;
1787: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &Btrans));
1788: PetscCall(MatCompare_SeqBAIJ_Private(A, Btrans, tol, f));
1789: PetscCall(MatDestroy(&Btrans));
1790: PetscFunctionReturn(PETSC_SUCCESS);
1791: }
1793: static PetscErrorCode MatEqual_SeqBAIJ(Mat A, Mat B, PetscBool *flg)
1794: {
1795: PetscFunctionBegin;
1796: PetscCall(MatCompare_SeqBAIJ_Private(A, B, 0.0, flg));
1797: PetscFunctionReturn(PETSC_SUCCESS);
1798: }
1800: /* Used for both SeqBAIJ and SeqSBAIJ matrices */
1801: PetscErrorCode MatView_SeqBAIJ_Binary(Mat mat, PetscViewer viewer)
1802: {
1803: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)mat->data;
1804: PetscInt header[4], M, N, m, bs, nz, cnt, i, j, k, l;
1805: PetscInt *rowlens, *colidxs;
1806: PetscScalar *matvals;
1808: PetscFunctionBegin;
1809: PetscCall(PetscViewerSetUp(viewer));
1811: M = mat->rmap->N;
1812: N = mat->cmap->N;
1813: m = mat->rmap->n;
1814: bs = mat->rmap->bs;
1815: nz = bs * bs * A->nz;
1817: /* write matrix header */
1818: header[0] = MAT_FILE_CLASSID;
1819: header[1] = M;
1820: header[2] = N;
1821: header[3] = nz;
1822: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1824: /* store row lengths */
1825: PetscCall(PetscMalloc1(m, &rowlens));
1826: for (cnt = 0, i = 0; i < A->mbs; i++)
1827: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i]);
1828: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
1829: PetscCall(PetscFree(rowlens));
1831: /* store column indices */
1832: PetscCall(PetscMalloc1(nz, &colidxs));
1833: for (cnt = 0, i = 0; i < A->mbs; i++)
1834: for (k = 0; k < bs; k++)
1835: for (j = A->i[i]; j < A->i[i + 1]; j++)
1836: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[j] + l;
1837: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz);
1838: PetscCall(PetscViewerBinaryWrite(viewer, colidxs, nz, PETSC_INT));
1839: PetscCall(PetscFree(colidxs));
1841: /* store nonzero values */
1842: PetscCall(PetscMalloc1(nz, &matvals));
1843: for (cnt = 0, i = 0; i < A->mbs; i++)
1844: for (k = 0; k < bs; k++)
1845: for (j = A->i[i]; j < A->i[i + 1]; j++)
1846: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * j + l) + k];
1847: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz);
1848: PetscCall(PetscViewerBinaryWrite(viewer, matvals, nz, PETSC_SCALAR));
1849: PetscCall(PetscFree(matvals));
1851: /* write block size option to the viewer's .info file */
1852: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1853: PetscFunctionReturn(PETSC_SUCCESS);
1854: }
1856: static PetscErrorCode MatView_SeqBAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
1857: {
1858: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1859: PetscInt i, bs = A->rmap->bs, k;
1861: PetscFunctionBegin;
1862: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1863: for (i = 0; i < a->mbs; i++) {
1864: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT "-%" PetscInt_FMT ":", i * bs, i * bs + bs - 1));
1865: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT "-%" PetscInt_FMT ") ", bs * a->j[k], bs * a->j[k] + bs - 1));
1866: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1867: }
1868: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1869: PetscFunctionReturn(PETSC_SUCCESS);
1870: }
1872: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A, PetscViewer viewer)
1873: {
1874: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1875: PetscInt i, j, bs = A->rmap->bs, k, l, bs2 = a->bs2;
1876: PetscViewerFormat format;
1878: PetscFunctionBegin;
1879: if (A->structure_only) {
1880: PetscCall(MatView_SeqBAIJ_ASCII_structonly(A, viewer));
1881: PetscFunctionReturn(PETSC_SUCCESS);
1882: }
1884: PetscCall(PetscViewerGetFormat(viewer, &format));
1885: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1886: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1887: } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1888: const char *matname;
1889: Mat aij;
1890: PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &aij));
1891: PetscCall(PetscObjectGetName((PetscObject)A, &matname));
1892: PetscCall(PetscObjectSetName((PetscObject)aij, matname));
1893: PetscCall(MatView(aij, viewer));
1894: PetscCall(MatDestroy(&aij));
1895: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1896: PetscFunctionReturn(PETSC_SUCCESS);
1897: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1898: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1899: for (i = 0; i < a->mbs; i++) {
1900: for (j = 0; j < bs; j++) {
1901: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j));
1902: for (k = a->i[i]; k < a->i[i + 1]; k++) {
1903: for (l = 0; l < bs; l++) {
1904: #if defined(PETSC_USE_COMPLEX)
1905: if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1906: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1907: } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1908: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1909: } else if (PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1910: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j])));
1911: }
1912: #else
1913: if (a->a[bs2 * k + l * bs + j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j]));
1914: #endif
1915: }
1916: }
1917: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1918: }
1919: }
1920: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1921: } else {
1922: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1923: for (i = 0; i < a->mbs; i++) {
1924: for (j = 0; j < bs; j++) {
1925: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j));
1926: for (k = a->i[i]; k < a->i[i + 1]; k++) {
1927: for (l = 0; l < bs; l++) {
1928: #if defined(PETSC_USE_COMPLEX)
1929: if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0) {
1930: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1931: } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0) {
1932: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1933: } else {
1934: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j])));
1935: }
1936: #else
1937: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j]));
1938: #endif
1939: }
1940: }
1941: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1942: }
1943: }
1944: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1945: }
1946: PetscCall(PetscViewerFlush(viewer));
1947: PetscFunctionReturn(PETSC_SUCCESS);
1948: }
1950: #include <petscdraw.h>
1951: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
1952: {
1953: Mat A = (Mat)Aa;
1954: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1955: PetscInt row, i, j, k, l, mbs = a->mbs, bs = A->rmap->bs, bs2 = a->bs2;
1956: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
1957: MatScalar *aa;
1958: PetscViewer viewer;
1959: PetscViewerFormat format;
1960: int color;
1962: PetscFunctionBegin;
1963: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
1964: PetscCall(PetscViewerGetFormat(viewer, &format));
1965: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
1967: /* loop over matrix elements drawing boxes */
1969: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1970: PetscDrawCollectiveBegin(draw);
1971: /* Blue for negative, Cyan for zero and Red for positive */
1972: color = PETSC_DRAW_BLUE;
1973: for (i = 0, row = 0; i < mbs; i++, row += bs) {
1974: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1975: y_l = A->rmap->N - row - 1.0;
1976: y_r = y_l + 1.0;
1977: x_l = a->j[j] * bs;
1978: x_r = x_l + 1.0;
1979: aa = a->a + j * bs2;
1980: for (k = 0; k < bs; k++) {
1981: for (l = 0; l < bs; l++) {
1982: if (PetscRealPart(*aa++) >= 0.) continue;
1983: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
1984: }
1985: }
1986: }
1987: }
1988: color = PETSC_DRAW_CYAN;
1989: for (i = 0, row = 0; i < mbs; i++, row += bs) {
1990: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1991: y_l = A->rmap->N - row - 1.0;
1992: y_r = y_l + 1.0;
1993: x_l = a->j[j] * bs;
1994: x_r = x_l + 1.0;
1995: aa = a->a + j * bs2;
1996: for (k = 0; k < bs; k++) {
1997: for (l = 0; l < bs; l++) {
1998: if (PetscRealPart(*aa++) != 0.) continue;
1999: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
2000: }
2001: }
2002: }
2003: }
2004: color = PETSC_DRAW_RED;
2005: for (i = 0, row = 0; i < mbs; i++, row += bs) {
2006: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2007: y_l = A->rmap->N - row - 1.0;
2008: y_r = y_l + 1.0;
2009: x_l = a->j[j] * bs;
2010: x_r = x_l + 1.0;
2011: aa = a->a + j * bs2;
2012: for (k = 0; k < bs; k++) {
2013: for (l = 0; l < bs; l++) {
2014: if (PetscRealPart(*aa++) <= 0.) continue;
2015: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
2016: }
2017: }
2018: }
2019: }
2020: PetscDrawCollectiveEnd(draw);
2021: } else {
2022: /* use contour shading to indicate magnitude of values */
2023: /* first determine max of all nonzero values */
2024: PetscReal minv = 0.0, maxv = 0.0;
2025: PetscDraw popup;
2027: for (i = 0; i < a->nz * a->bs2; i++) {
2028: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
2029: }
2030: if (minv >= maxv) maxv = minv + PETSC_SMALL;
2031: PetscCall(PetscDrawGetPopup(draw, &popup));
2032: PetscCall(PetscDrawScalePopup(popup, 0.0, maxv));
2034: PetscDrawCollectiveBegin(draw);
2035: for (i = 0, row = 0; i < mbs; i++, row += bs) {
2036: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2037: y_l = A->rmap->N - row - 1.0;
2038: y_r = y_l + 1.0;
2039: x_l = a->j[j] * bs;
2040: x_r = x_l + 1.0;
2041: aa = a->a + j * bs2;
2042: for (k = 0; k < bs; k++) {
2043: for (l = 0; l < bs; l++) {
2044: MatScalar v = *aa++;
2045: color = PetscDrawRealToColor(PetscAbsScalar(v), minv, maxv);
2046: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
2047: }
2048: }
2049: }
2050: }
2051: PetscDrawCollectiveEnd(draw);
2052: }
2053: PetscFunctionReturn(PETSC_SUCCESS);
2054: }
2056: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A, PetscViewer viewer)
2057: {
2058: PetscReal xl, yl, xr, yr, w, h;
2059: PetscDraw draw;
2060: PetscBool isnull;
2062: PetscFunctionBegin;
2063: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
2064: PetscCall(PetscDrawIsNull(draw, &isnull));
2065: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
2067: xr = A->cmap->n;
2068: yr = A->rmap->N;
2069: h = yr / 10.0;
2070: w = xr / 10.0;
2071: xr += w;
2072: yr += h;
2073: xl = -w;
2074: yl = -h;
2075: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
2076: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
2077: PetscCall(PetscDrawZoom(draw, MatView_SeqBAIJ_Draw_Zoom, A));
2078: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
2079: PetscCall(PetscDrawSave(draw));
2080: PetscFunctionReturn(PETSC_SUCCESS);
2081: }
2083: PetscErrorCode MatView_SeqBAIJ(Mat A, PetscViewer viewer)
2084: {
2085: PetscBool isascii, isbinary, isdraw;
2087: PetscFunctionBegin;
2088: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
2089: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
2090: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
2091: if (isascii) {
2092: PetscCall(MatView_SeqBAIJ_ASCII(A, viewer));
2093: } else if (isbinary) {
2094: PetscCall(MatView_SeqBAIJ_Binary(A, viewer));
2095: } else if (isdraw) {
2096: PetscCall(MatView_SeqBAIJ_Draw(A, viewer));
2097: } else {
2098: Mat B;
2099: PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &B));
2100: PetscCall(MatView(B, viewer));
2101: PetscCall(MatDestroy(&B));
2102: }
2103: PetscFunctionReturn(PETSC_SUCCESS);
2104: }
2106: PetscErrorCode MatGetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
2107: {
2108: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2109: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
2110: PetscInt *ai = a->i, *ailen = a->ilen;
2111: PetscInt brow, bcol, ridx, cidx, bs = A->rmap->bs, bs2 = a->bs2;
2112: MatScalar *ap, *aa = a->a;
2114: PetscFunctionBegin;
2115: for (k = 0; k < m; k++) { /* loop over rows */
2116: row = im[k];
2117: brow = row / bs;
2118: if (row < 0) {
2119: v += n;
2120: continue;
2121: } /* negative row */
2122: PetscCheck(row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " too large", row);
2123: rp = PetscSafePointerPlusOffset(aj, ai[brow]);
2124: ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]);
2125: nrow = ailen[brow];
2126: for (l = 0; l < n; l++) { /* loop over columns */
2127: if (in[l] < 0) {
2128: v++;
2129: continue;
2130: } /* negative column */
2131: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column %" PetscInt_FMT " too large", in[l]);
2132: col = in[l];
2133: bcol = col / bs;
2134: cidx = col % bs;
2135: ridx = row % bs;
2136: high = nrow;
2137: low = 0; /* assume unsorted */
2138: while (high - low > 5) {
2139: t = (low + high) / 2;
2140: if (rp[t] > bcol) high = t;
2141: else low = t;
2142: }
2143: for (i = low; i < high; i++) {
2144: if (rp[i] > bcol) break;
2145: if (rp[i] == bcol) {
2146: *v++ = ap[bs2 * i + bs * cidx + ridx];
2147: goto finished;
2148: }
2149: }
2150: *v++ = 0.0;
2151: finished:;
2152: }
2153: }
2154: PetscFunctionReturn(PETSC_SUCCESS);
2155: }
2157: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
2158: {
2159: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2160: PetscInt *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, rmax, N, lastcol = -1;
2161: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
2162: PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs, stepval;
2163: PetscBool roworiented = a->roworiented;
2164: const PetscScalar *value = v;
2165: MatScalar *ap = NULL, *aa = a->a, *bap;
2167: PetscFunctionBegin;
2168: if (roworiented) {
2169: stepval = (n - 1) * bs;
2170: } else {
2171: stepval = (m - 1) * bs;
2172: }
2173: for (k = 0; k < m; k++) { /* loop over added rows */
2174: row = im[k];
2175: if (row < 0) continue;
2176: PetscCheck(row < a->mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block row index too large %" PetscInt_FMT " max %" PetscInt_FMT, row, a->mbs - 1);
2177: rp = aj + ai[row];
2178: if (!A->structure_only) ap = aa + bs2 * ai[row];
2179: rmax = imax[row];
2180: nrow = ailen[row];
2181: low = 0;
2182: high = nrow;
2183: for (l = 0; l < n; l++) { /* loop over added columns */
2184: if (in[l] < 0) continue;
2185: PetscCheck(in[l] < a->nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block column index too large %" PetscInt_FMT " max %" PetscInt_FMT, in[l], a->nbs - 1);
2186: col = in[l];
2187: if (!A->structure_only) {
2188: if (roworiented) {
2189: value = v + (k * (stepval + bs) + l) * bs;
2190: } else {
2191: value = v + (l * (stepval + bs) + k) * bs;
2192: }
2193: }
2194: if (col <= lastcol) low = 0;
2195: else high = nrow;
2196: lastcol = col;
2197: while (high - low > 7) {
2198: t = (low + high) / 2;
2199: if (rp[t] > col) high = t;
2200: else low = t;
2201: }
2202: for (i = low; i < high; i++) {
2203: if (rp[i] > col) break;
2204: if (rp[i] == col) {
2205: if (A->structure_only) goto noinsert2;
2206: bap = ap + bs2 * i;
2207: if (roworiented) {
2208: if (is == ADD_VALUES) {
2209: for (ii = 0; ii < bs; ii++, value += stepval) {
2210: for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
2211: }
2212: } else {
2213: for (ii = 0; ii < bs; ii++, value += stepval) {
2214: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
2215: }
2216: }
2217: } else {
2218: if (is == ADD_VALUES) {
2219: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
2220: for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
2221: bap += bs;
2222: }
2223: } else {
2224: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
2225: for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
2226: bap += bs;
2227: }
2228: }
2229: }
2230: goto noinsert2;
2231: }
2232: }
2233: if (nonew == 1) goto noinsert2;
2234: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new blocked index new nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col);
2235: if (A->structure_only) {
2236: MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
2237: } else {
2238: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
2239: }
2240: N = nrow++ - 1;
2241: high++;
2242: /* shift up all the later entries in this row */
2243: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
2244: rp[i] = col;
2245: if (!A->structure_only) {
2246: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
2247: bap = ap + bs2 * i;
2248: if (roworiented) {
2249: for (ii = 0; ii < bs; ii++, value += stepval) {
2250: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
2251: }
2252: } else {
2253: for (ii = 0; ii < bs; ii++, value += stepval) {
2254: for (jj = 0; jj < bs; jj++) *bap++ = *value++;
2255: }
2256: }
2257: }
2258: noinsert2:;
2259: low = i;
2260: }
2261: ailen[row] = nrow;
2262: }
2263: PetscFunctionReturn(PETSC_SUCCESS);
2264: }
2266: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A, MatAssemblyType mode)
2267: {
2268: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2269: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
2270: PetscInt m = A->rmap->N, *ip, N, *ailen = a->ilen;
2271: PetscInt mbs = a->mbs, bs2 = a->bs2, rmax = 0;
2272: MatScalar *aa = a->a, *ap;
2273: PetscReal ratio = 0.6;
2275: PetscFunctionBegin;
2276: if (mode == MAT_FLUSH_ASSEMBLY || (A->was_assembled && A->ass_nonzerostate == A->nonzerostate)) PetscFunctionReturn(PETSC_SUCCESS);
2278: if (m) rmax = ailen[0];
2279: for (i = 1; i < mbs; i++) {
2280: /* move each row back by the amount of empty slots (fshift) before it*/
2281: fshift += imax[i - 1] - ailen[i - 1];
2282: rmax = PetscMax(rmax, ailen[i]);
2283: if (fshift) {
2284: ip = aj + ai[i];
2285: ap = aa + bs2 * ai[i];
2286: N = ailen[i];
2287: PetscCall(PetscArraymove(ip - fshift, ip, N));
2288: if (!A->structure_only) PetscCall(PetscArraymove(ap - bs2 * fshift, ap, bs2 * N));
2289: }
2290: ai[i] = ai[i - 1] + ailen[i - 1];
2291: }
2292: if (mbs) {
2293: fshift += imax[mbs - 1] - ailen[mbs - 1];
2294: ai[mbs] = ai[mbs - 1] + ailen[mbs - 1];
2295: }
2297: /* reset ilen and imax for each row */
2298: a->nonzerorowcnt = 0;
2299: if (A->structure_only) {
2300: PetscCall(PetscFree2(a->imax, a->ilen));
2301: } else { /* !A->structure_only */
2302: for (i = 0; i < mbs; i++) {
2303: ailen[i] = imax[i] = ai[i + 1] - ai[i];
2304: a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
2305: }
2306: }
2307: a->nz = ai[mbs];
2309: /* diagonals may have moved, so kill the diagonal pointers */
2310: a->idiagvalid = PETSC_FALSE;
2311: if (fshift && a->diag) PetscCall(PetscFree(a->diag));
2312: if (fshift) PetscCheck(a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT " block size %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, A->rmap->bs, fshift * bs2);
2313: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT ", block size %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded, %" PetscInt_FMT " used\n", m, A->cmap->n, A->rmap->bs, fshift * bs2, a->nz * bs2));
2314: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues is %" PetscInt_FMT "\n", a->reallocs));
2315: PetscCall(PetscInfo(A, "Most nonzeros blocks in any row is %" PetscInt_FMT "\n", rmax));
2317: A->info.mallocs += a->reallocs;
2318: a->reallocs = 0;
2319: A->info.nz_unneeded = (PetscReal)fshift * bs2;
2320: a->rmax = rmax;
2322: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, mbs, ratio));
2323: PetscFunctionReturn(PETSC_SUCCESS);
2324: }
2326: /*
2327: This function returns an array of flags which indicate the locations of contiguous
2328: blocks that should be zeroed. for eg: if bs = 3 and is = [0,1,2,3,5,6,7,8,9]
2329: then the resulting sizes = [3,1,1,3,1] corresponding to sets [(0,1,2),(3),(5),(6,7,8),(9)]
2330: Assume: sizes should be long enough to hold all the values.
2331: */
2332: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[], PetscInt n, PetscInt bs, PetscInt sizes[], PetscInt *bs_max)
2333: {
2334: PetscInt j = 0;
2336: PetscFunctionBegin;
2337: for (PetscInt i = 0; i < n; j++) {
2338: PetscInt row = idx[i];
2339: if (row % bs != 0) { /* Not the beginning of a block */
2340: sizes[j] = 1;
2341: i++;
2342: } else if (i + bs > n) { /* complete block doesn't exist (at idx end) */
2343: sizes[j] = 1; /* Also makes sure at least 'bs' values exist for next else */
2344: i++;
2345: } else { /* Beginning of the block, so check if the complete block exists */
2346: PetscBool flg = PETSC_TRUE;
2347: for (PetscInt k = 1; k < bs; k++) {
2348: if (row + k != idx[i + k]) { /* break in the block */
2349: flg = PETSC_FALSE;
2350: break;
2351: }
2352: }
2353: if (flg) { /* No break in the bs */
2354: sizes[j] = bs;
2355: i += bs;
2356: } else {
2357: sizes[j] = 1;
2358: i++;
2359: }
2360: }
2361: }
2362: *bs_max = j;
2363: PetscFunctionReturn(PETSC_SUCCESS);
2364: }
2366: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b)
2367: {
2368: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)A->data;
2369: PetscInt i, j, k, count, *rows;
2370: PetscInt bs = A->rmap->bs, bs2 = baij->bs2, *sizes, row, bs_max;
2371: PetscScalar zero = 0.0;
2372: MatScalar *aa;
2373: const PetscScalar *xx;
2374: PetscScalar *bb;
2376: PetscFunctionBegin;
2377: /* fix right-hand side if needed */
2378: if (x && b) {
2379: PetscCall(VecGetArrayRead(x, &xx));
2380: PetscCall(VecGetArray(b, &bb));
2381: for (i = 0; i < is_n; i++) bb[is_idx[i]] = diag * xx[is_idx[i]];
2382: PetscCall(VecRestoreArrayRead(x, &xx));
2383: PetscCall(VecRestoreArray(b, &bb));
2384: }
2386: /* Make a copy of the IS and sort it */
2387: /* allocate memory for rows,sizes */
2388: PetscCall(PetscMalloc2(is_n, &rows, 2 * is_n, &sizes));
2390: /* copy IS values to rows, and sort them */
2391: for (i = 0; i < is_n; i++) rows[i] = is_idx[i];
2392: PetscCall(PetscSortInt(is_n, rows));
2394: if (baij->keepnonzeropattern) {
2395: for (i = 0; i < is_n; i++) sizes[i] = 1;
2396: bs_max = is_n;
2397: } else {
2398: PetscCall(MatZeroRows_SeqBAIJ_Check_Blocks(rows, is_n, bs, sizes, &bs_max));
2399: A->nonzerostate++;
2400: }
2402: for (i = 0, j = 0; i < bs_max; j += sizes[i], i++) {
2403: row = rows[j];
2404: PetscCheck(row >= 0 && row <= A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", row);
2405: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
2406: aa = baij->a + baij->i[row / bs] * bs2 + (row % bs);
2407: if (sizes[i] == bs && !baij->keepnonzeropattern) {
2408: if (diag != (PetscScalar)0.0) {
2409: if (baij->ilen[row / bs] > 0) {
2410: baij->ilen[row / bs] = 1;
2411: baij->j[baij->i[row / bs]] = row / bs;
2413: PetscCall(PetscArrayzero(aa, count * bs));
2414: }
2415: /* Now insert all the diagonal values for this bs */
2416: for (k = 0; k < bs; k++) PetscUseTypeMethod(A, setvalues, 1, rows + j + k, 1, rows + j + k, &diag, INSERT_VALUES);
2417: } else { /* (diag == 0.0) */
2418: baij->ilen[row / bs] = 0;
2419: } /* end (diag == 0.0) */
2420: } else { /* (sizes[i] != bs) */
2421: PetscAssert(sizes[i] == 1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal Error. Value should be 1");
2422: for (k = 0; k < count; k++) {
2423: aa[0] = zero;
2424: aa += bs;
2425: }
2426: if (diag != (PetscScalar)0.0) PetscUseTypeMethod(A, setvalues, 1, rows + j, 1, rows + j, &diag, INSERT_VALUES);
2427: }
2428: }
2430: PetscCall(PetscFree2(rows, sizes));
2431: PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY));
2432: PetscFunctionReturn(PETSC_SUCCESS);
2433: }
2435: static PetscErrorCode MatZeroRowsColumns_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b)
2436: {
2437: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)A->data;
2438: PetscInt i, j, k, count;
2439: PetscInt bs = A->rmap->bs, bs2 = baij->bs2, row, col;
2440: PetscScalar zero = 0.0;
2441: MatScalar *aa;
2442: const PetscScalar *xx;
2443: PetscScalar *bb;
2444: PetscBool *zeroed, vecs = PETSC_FALSE;
2446: PetscFunctionBegin;
2447: /* fix right-hand side if needed */
2448: if (x && b) {
2449: PetscCall(VecGetArrayRead(x, &xx));
2450: PetscCall(VecGetArray(b, &bb));
2451: vecs = PETSC_TRUE;
2452: }
2454: /* zero the columns */
2455: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2456: for (i = 0; i < is_n; i++) {
2457: PetscCheck(is_idx[i] >= 0 && is_idx[i] < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", is_idx[i]);
2458: zeroed[is_idx[i]] = PETSC_TRUE;
2459: }
2460: for (i = 0; i < A->rmap->N; i++) {
2461: if (!zeroed[i]) {
2462: row = i / bs;
2463: for (j = baij->i[row]; j < baij->i[row + 1]; j++) {
2464: for (k = 0; k < bs; k++) {
2465: col = bs * baij->j[j] + k;
2466: if (zeroed[col]) {
2467: aa = baij->a + j * bs2 + (i % bs) + bs * k;
2468: if (vecs) bb[i] -= aa[0] * xx[col];
2469: aa[0] = 0.0;
2470: }
2471: }
2472: }
2473: } else if (vecs) bb[i] = diag * xx[i];
2474: }
2475: PetscCall(PetscFree(zeroed));
2476: if (vecs) {
2477: PetscCall(VecRestoreArrayRead(x, &xx));
2478: PetscCall(VecRestoreArray(b, &bb));
2479: }
2481: /* zero the rows */
2482: for (i = 0; i < is_n; i++) {
2483: row = is_idx[i];
2484: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
2485: aa = baij->a + baij->i[row / bs] * bs2 + (row % bs);
2486: for (k = 0; k < count; k++) {
2487: aa[0] = zero;
2488: aa += bs;
2489: }
2490: if (diag != (PetscScalar)0.0) PetscUseTypeMethod(A, setvalues, 1, &row, 1, &row, &diag, INSERT_VALUES);
2491: }
2492: PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY));
2493: PetscFunctionReturn(PETSC_SUCCESS);
2494: }
2496: PetscErrorCode MatSetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
2497: {
2498: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2499: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N, lastcol = -1;
2500: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
2501: PetscInt *aj = a->j, nonew = a->nonew, bs = A->rmap->bs, brow, bcol;
2502: PetscInt ridx, cidx, bs2 = a->bs2;
2503: PetscBool roworiented = a->roworiented;
2504: MatScalar *ap = NULL, value = 0.0, *aa = a->a, *bap;
2506: PetscFunctionBegin;
2507: for (k = 0; k < m; k++) { /* loop over added rows */
2508: row = im[k];
2509: brow = row / bs;
2510: if (row < 0) continue;
2511: 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);
2512: rp = PetscSafePointerPlusOffset(aj, ai[brow]);
2513: if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]);
2514: rmax = imax[brow];
2515: nrow = ailen[brow];
2516: low = 0;
2517: high = nrow;
2518: for (l = 0; l < n; l++) { /* loop over added columns */
2519: if (in[l] < 0) continue;
2520: 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);
2521: col = in[l];
2522: bcol = col / bs;
2523: ridx = row % bs;
2524: cidx = col % bs;
2525: if (!A->structure_only) {
2526: if (roworiented) {
2527: value = v[l + k * n];
2528: } else {
2529: value = v[k + l * m];
2530: }
2531: }
2532: if (col <= lastcol) low = 0;
2533: else high = nrow;
2534: lastcol = col;
2535: while (high - low > 7) {
2536: t = (low + high) / 2;
2537: if (rp[t] > bcol) high = t;
2538: else low = t;
2539: }
2540: for (i = low; i < high; i++) {
2541: if (rp[i] > bcol) break;
2542: if (rp[i] == bcol) {
2543: bap = PetscSafePointerPlusOffset(ap, bs2 * i + bs * cidx + ridx);
2544: if (!A->structure_only) {
2545: if (is == ADD_VALUES) *bap += value;
2546: else *bap = value;
2547: }
2548: goto noinsert1;
2549: }
2550: }
2551: if (nonew == 1) goto noinsert1;
2552: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col);
2553: if (A->structure_only) {
2554: MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, brow, bcol, rmax, ai, aj, rp, imax, nonew, MatScalar);
2555: } else {
2556: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
2557: }
2558: N = nrow++ - 1;
2559: high++;
2560: /* shift up all the later entries in this row */
2561: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
2562: rp[i] = bcol;
2563: if (!A->structure_only) {
2564: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
2565: PetscCall(PetscArrayzero(ap + bs2 * i, bs2));
2566: ap[bs2 * i + bs * cidx + ridx] = value;
2567: }
2568: a->nz++;
2569: noinsert1:;
2570: low = i;
2571: }
2572: ailen[brow] = nrow;
2573: }
2574: PetscFunctionReturn(PETSC_SUCCESS);
2575: }
2577: static PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2578: {
2579: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inA->data;
2580: Mat outA;
2581: PetscBool row_identity, col_identity;
2583: PetscFunctionBegin;
2584: 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));
2587: PetscCheck(row_identity && col_identity, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Row and column permutations must be identity for in-place ILU");
2589: outA = inA;
2590: inA->factortype = MAT_FACTOR_LU;
2591: PetscCall(PetscFree(inA->solvertype));
2592: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2594: PetscCall(MatMarkDiagonal_SeqBAIJ(inA));
2596: PetscCall(PetscObjectReference((PetscObject)row));
2597: PetscCall(ISDestroy(&a->row));
2598: a->row = row;
2599: PetscCall(PetscObjectReference((PetscObject)col));
2600: PetscCall(ISDestroy(&a->col));
2601: a->col = col;
2603: /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
2604: PetscCall(ISDestroy(&a->icol));
2605: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2607: PetscCall(MatSeqBAIJSetNumericFactorization_inplace(inA, (PetscBool)(row_identity && col_identity)));
2608: if (!a->solve_work) PetscCall(PetscMalloc1(inA->rmap->N + inA->rmap->bs, &a->solve_work));
2609: PetscCall(MatLUFactorNumeric(outA, inA, info));
2610: PetscFunctionReturn(PETSC_SUCCESS);
2611: }
2613: static PetscErrorCode MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat, const PetscInt *indices)
2614: {
2615: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;
2617: PetscFunctionBegin;
2618: baij->nz = baij->maxnz;
2619: PetscCall(PetscArraycpy(baij->j, indices, baij->nz));
2620: PetscCall(PetscArraycpy(baij->ilen, baij->imax, baij->mbs));
2621: PetscFunctionReturn(PETSC_SUCCESS);
2622: }
2624: /*@
2625: MatSeqBAIJSetColumnIndices - Set the column indices for all the block rows in the matrix.
2627: Input Parameters:
2628: + mat - the `MATSEQBAIJ` matrix
2629: - indices - the block column indices
2631: Level: advanced
2633: Notes:
2634: This can be called if you have precomputed the nonzero structure of the
2635: matrix and want to provide it to the matrix object to improve the performance
2636: of the `MatSetValues()` operation.
2638: You MUST have set the correct numbers of nonzeros per row in the call to
2639: `MatCreateSeqBAIJ()`, and the columns indices MUST be sorted.
2641: MUST be called before any calls to `MatSetValues()`
2643: .seealso: [](ch_matrices), `Mat`, `MATSEQBAIJ`, `MatSetValues()`
2644: @*/
2645: PetscErrorCode MatSeqBAIJSetColumnIndices(Mat mat, PetscInt *indices)
2646: {
2647: PetscFunctionBegin;
2649: PetscAssertPointer(indices, 2);
2650: PetscUseMethod(mat, "MatSeqBAIJSetColumnIndices_C", (Mat, const PetscInt *), (mat, (const PetscInt *)indices));
2651: PetscFunctionReturn(PETSC_SUCCESS);
2652: }
2654: static PetscErrorCode MatGetRowMaxAbs_SeqBAIJ(Mat A, Vec v, PetscInt idx[])
2655: {
2656: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2657: PetscInt i, j, n, row, bs, *ai, *aj, mbs;
2658: PetscReal atmp;
2659: PetscScalar *x, zero = 0.0;
2660: MatScalar *aa;
2661: PetscInt ncols, brow, krow, kcol;
2663: PetscFunctionBegin;
2664: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2665: bs = A->rmap->bs;
2666: aa = a->a;
2667: ai = a->i;
2668: aj = a->j;
2669: mbs = a->mbs;
2671: PetscCall(VecSet(v, zero));
2672: PetscCall(VecGetArray(v, &x));
2673: PetscCall(VecGetLocalSize(v, &n));
2674: PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
2675: for (i = 0; i < mbs; i++) {
2676: ncols = ai[1] - ai[0];
2677: ai++;
2678: brow = bs * i;
2679: for (j = 0; j < ncols; j++) {
2680: for (kcol = 0; kcol < bs; kcol++) {
2681: for (krow = 0; krow < bs; krow++) {
2682: atmp = PetscAbsScalar(*aa);
2683: aa++;
2684: row = brow + krow; /* row index */
2685: if (PetscAbsScalar(x[row]) < atmp) {
2686: x[row] = atmp;
2687: if (idx) idx[row] = bs * (*aj) + kcol;
2688: }
2689: }
2690: }
2691: aj++;
2692: }
2693: }
2694: PetscCall(VecRestoreArray(v, &x));
2695: PetscFunctionReturn(PETSC_SUCCESS);
2696: }
2698: static PetscErrorCode MatGetRowSumAbs_SeqBAIJ(Mat A, Vec v)
2699: {
2700: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2701: PetscInt i, j, n, row, bs, *ai, mbs;
2702: PetscReal atmp;
2703: PetscScalar *x, zero = 0.0;
2704: MatScalar *aa;
2705: PetscInt ncols, brow, krow, kcol;
2707: PetscFunctionBegin;
2708: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2709: bs = A->rmap->bs;
2710: aa = a->a;
2711: ai = a->i;
2712: mbs = a->mbs;
2714: PetscCall(VecSet(v, zero));
2715: PetscCall(VecGetArrayWrite(v, &x));
2716: PetscCall(VecGetLocalSize(v, &n));
2717: PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
2718: for (i = 0; i < mbs; i++) {
2719: ncols = ai[1] - ai[0];
2720: ai++;
2721: brow = bs * i;
2722: for (j = 0; j < ncols; j++) {
2723: for (kcol = 0; kcol < bs; kcol++) {
2724: for (krow = 0; krow < bs; krow++) {
2725: atmp = PetscAbsScalar(*aa);
2726: aa++;
2727: row = brow + krow; /* row index */
2728: x[row] += atmp;
2729: }
2730: }
2731: }
2732: }
2733: PetscCall(VecRestoreArrayWrite(v, &x));
2734: PetscFunctionReturn(PETSC_SUCCESS);
2735: }
2737: static PetscErrorCode MatCopy_SeqBAIJ(Mat A, Mat B, MatStructure str)
2738: {
2739: PetscFunctionBegin;
2740: /* If the two matrices have the same copy implementation, use fast copy. */
2741: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2742: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2743: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data;
2744: PetscInt ambs = a->mbs, bmbs = b->mbs, abs = A->rmap->bs, bbs = B->rmap->bs, bs2 = abs * abs;
2746: PetscCheck(a->i[ambs] == b->i[bmbs], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzero blocks in matrices A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", a->i[ambs], b->i[bmbs]);
2747: PetscCheck(abs == bbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Block size A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", abs, bbs);
2748: PetscCall(PetscArraycpy(b->a, a->a, bs2 * a->i[ambs]));
2749: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2750: } else {
2751: PetscCall(MatCopy_Basic(A, B, str));
2752: }
2753: PetscFunctionReturn(PETSC_SUCCESS);
2754: }
2756: static PetscErrorCode MatSeqBAIJGetArray_SeqBAIJ(Mat A, PetscScalar *array[])
2757: {
2758: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2760: PetscFunctionBegin;
2761: *array = a->a;
2762: PetscFunctionReturn(PETSC_SUCCESS);
2763: }
2765: static PetscErrorCode MatSeqBAIJRestoreArray_SeqBAIJ(Mat A, PetscScalar *array[])
2766: {
2767: PetscFunctionBegin;
2768: *array = NULL;
2769: PetscFunctionReturn(PETSC_SUCCESS);
2770: }
2772: PetscErrorCode MatAXPYGetPreallocation_SeqBAIJ(Mat Y, Mat X, PetscInt *nnz)
2773: {
2774: PetscInt bs = Y->rmap->bs, mbs = Y->rmap->N / bs;
2775: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
2776: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
2778: PetscFunctionBegin;
2779: /* Set the number of nonzeros in the new matrix */
2780: PetscCall(MatAXPYGetPreallocation_SeqX_private(mbs, x->i, x->j, y->i, y->j, nnz));
2781: PetscFunctionReturn(PETSC_SUCCESS);
2782: }
2784: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2785: {
2786: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data, *y = (Mat_SeqBAIJ *)Y->data;
2787: PetscInt bs = Y->rmap->bs, bs2 = bs * bs;
2788: PetscBLASInt one = 1;
2790: PetscFunctionBegin;
2791: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
2792: PetscBool e = x->nz == y->nz && x->mbs == y->mbs && bs == X->rmap->bs ? PETSC_TRUE : PETSC_FALSE;
2793: if (e) {
2794: PetscCall(PetscArraycmp(x->i, y->i, x->mbs + 1, &e));
2795: if (e) {
2796: PetscCall(PetscArraycmp(x->j, y->j, x->i[x->mbs], &e));
2797: if (e) str = SAME_NONZERO_PATTERN;
2798: }
2799: }
2800: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
2801: }
2802: if (str == SAME_NONZERO_PATTERN) {
2803: PetscScalar alpha = a;
2804: PetscBLASInt bnz;
2805: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
2806: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
2807: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
2808: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2809: PetscCall(MatAXPY_Basic(Y, a, X, str));
2810: } else {
2811: Mat B;
2812: PetscInt *nnz;
2813: PetscCheck(bs == X->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrices must have same block size");
2814: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
2815: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2816: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2817: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
2818: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
2819: PetscCall(MatSetType(B, (MatType)((PetscObject)Y)->type_name));
2820: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(Y, X, nnz));
2821: PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz));
2822: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2823: PetscCall(MatHeaderMerge(Y, &B));
2824: PetscCall(PetscFree(nnz));
2825: }
2826: PetscFunctionReturn(PETSC_SUCCESS);
2827: }
2829: PETSC_INTERN PetscErrorCode MatConjugate_SeqBAIJ(Mat A)
2830: {
2831: #if PetscDefined(USE_COMPLEX)
2832: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2833: PetscInt i, nz = a->bs2 * a->i[a->mbs];
2834: MatScalar *aa = a->a;
2836: PetscFunctionBegin;
2837: for (i = 0; i < nz; i++) aa[i] = PetscConj(aa[i]);
2838: PetscFunctionReturn(PETSC_SUCCESS);
2839: #else
2840: (void)A;
2841: return PETSC_SUCCESS;
2842: #endif
2843: }
2845: static PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2846: {
2847: #if PetscDefined(USE_COMPLEX)
2848: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2849: PetscInt i, nz = a->bs2 * a->i[a->mbs];
2850: MatScalar *aa = a->a;
2852: PetscFunctionBegin;
2853: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
2854: PetscFunctionReturn(PETSC_SUCCESS);
2855: #else
2856: (void)A;
2857: return PETSC_SUCCESS;
2858: #endif
2859: }
2861: static PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2862: {
2863: #if PetscDefined(USE_COMPLEX)
2864: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2865: PetscInt i, nz = a->bs2 * a->i[a->mbs];
2866: MatScalar *aa = a->a;
2868: PetscFunctionBegin;
2869: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2870: PetscFunctionReturn(PETSC_SUCCESS);
2871: #else
2872: (void)A;
2873: return PETSC_SUCCESS;
2874: #endif
2875: }
2877: /*
2878: Code almost identical to MatGetColumnIJ_SeqAIJ() should share common code
2879: */
2880: static PetscErrorCode MatGetColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
2881: {
2882: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2883: PetscInt bs = A->rmap->bs, i, *collengths, *cia, *cja, n = A->cmap->n / bs, m = A->rmap->n / bs;
2884: PetscInt nz = a->i[m], row, *jj, mr, col;
2886: PetscFunctionBegin;
2887: *nn = n;
2888: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2889: PetscCheck(!symmetric, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not for BAIJ matrices");
2890: PetscCall(PetscCalloc1(n, &collengths));
2891: PetscCall(PetscMalloc1(n + 1, &cia));
2892: PetscCall(PetscMalloc1(nz, &cja));
2893: jj = a->j;
2894: for (i = 0; i < nz; i++) collengths[jj[i]]++;
2895: cia[0] = oshift;
2896: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
2897: PetscCall(PetscArrayzero(collengths, n));
2898: jj = a->j;
2899: for (row = 0; row < m; row++) {
2900: mr = a->i[row + 1] - a->i[row];
2901: for (i = 0; i < mr; i++) {
2902: col = *jj++;
2904: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2905: }
2906: }
2907: PetscCall(PetscFree(collengths));
2908: *ia = cia;
2909: *ja = cja;
2910: PetscFunctionReturn(PETSC_SUCCESS);
2911: }
2913: static PetscErrorCode MatRestoreColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
2914: {
2915: PetscFunctionBegin;
2916: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2917: PetscCall(PetscFree(*ia));
2918: PetscCall(PetscFree(*ja));
2919: PetscFunctionReturn(PETSC_SUCCESS);
2920: }
2922: /*
2923: MatGetColumnIJ_SeqBAIJ_Color() and MatRestoreColumnIJ_SeqBAIJ_Color() are customized from
2924: MatGetColumnIJ_SeqBAIJ() and MatRestoreColumnIJ_SeqBAIJ() by adding an output
2925: spidx[], index of a->a, to be used in MatTransposeColoringCreate() and MatFDColoringCreate()
2926: */
2927: PetscErrorCode MatGetColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
2928: {
2929: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2930: PetscInt i, *collengths, *cia, *cja, n = a->nbs, m = a->mbs;
2931: PetscInt nz = a->i[m], row, *jj, mr, col;
2932: PetscInt *cspidx;
2934: PetscFunctionBegin;
2935: *nn = n;
2936: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2938: PetscCall(PetscCalloc1(n, &collengths));
2939: PetscCall(PetscMalloc1(n + 1, &cia));
2940: PetscCall(PetscMalloc1(nz, &cja));
2941: PetscCall(PetscMalloc1(nz, &cspidx));
2942: jj = a->j;
2943: for (i = 0; i < nz; i++) collengths[jj[i]]++;
2944: cia[0] = oshift;
2945: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
2946: PetscCall(PetscArrayzero(collengths, n));
2947: jj = a->j;
2948: for (row = 0; row < m; row++) {
2949: mr = a->i[row + 1] - a->i[row];
2950: for (i = 0; i < mr; i++) {
2951: col = *jj++;
2952: cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
2953: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2954: }
2955: }
2956: PetscCall(PetscFree(collengths));
2957: *ia = cia;
2958: *ja = cja;
2959: *spidx = cspidx;
2960: PetscFunctionReturn(PETSC_SUCCESS);
2961: }
2963: PetscErrorCode MatRestoreColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
2964: {
2965: PetscFunctionBegin;
2966: PetscCall(MatRestoreColumnIJ_SeqBAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
2967: PetscCall(PetscFree(*spidx));
2968: PetscFunctionReturn(PETSC_SUCCESS);
2969: }
2971: static PetscErrorCode MatShift_SeqBAIJ(Mat Y, PetscScalar a)
2972: {
2973: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)Y->data;
2975: PetscFunctionBegin;
2976: if (!Y->preallocated || !aij->nz) PetscCall(MatSeqBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL));
2977: PetscCall(MatShift_Basic(Y, a));
2978: PetscFunctionReturn(PETSC_SUCCESS);
2979: }
2981: PetscErrorCode MatEliminateZeros_SeqBAIJ(Mat A, PetscBool keep)
2982: {
2983: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2984: PetscInt fshift = 0, fshift_prev = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax, j, k;
2985: PetscInt m = A->rmap->N, *ailen = a->ilen;
2986: PetscInt mbs = a->mbs, bs2 = a->bs2, rmax = 0;
2987: MatScalar *aa = a->a, *ap;
2988: PetscBool zero;
2990: PetscFunctionBegin;
2991: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
2992: if (m) rmax = ailen[0];
2993: for (i = 1; i <= mbs; i++) {
2994: for (k = ai[i - 1]; k < ai[i]; k++) {
2995: zero = PETSC_TRUE;
2996: ap = aa + bs2 * k;
2997: for (j = 0; j < bs2 && zero; j++) {
2998: if (ap[j] != 0.0) zero = PETSC_FALSE;
2999: }
3000: if (zero && (aj[k] != i - 1 || !keep)) fshift++;
3001: else {
3002: if (zero && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal block at row %" PetscInt_FMT "\n", i - 1));
3003: aj[k - fshift] = aj[k];
3004: PetscCall(PetscArraymove(ap - bs2 * fshift, ap, bs2));
3005: }
3006: }
3007: ai[i - 1] -= fshift_prev;
3008: fshift_prev = fshift;
3009: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
3010: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
3011: rmax = PetscMax(rmax, ailen[i - 1]);
3012: }
3013: if (fshift) {
3014: if (mbs) {
3015: ai[mbs] -= fshift;
3016: a->nz = ai[mbs];
3017: }
3018: 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));
3019: A->nonzerostate++;
3020: A->info.nz_unneeded += (PetscReal)fshift;
3021: a->rmax = rmax;
3022: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
3023: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
3024: }
3025: PetscFunctionReturn(PETSC_SUCCESS);
3026: }
3028: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
3029: MatGetRow_SeqBAIJ,
3030: MatRestoreRow_SeqBAIJ,
3031: MatMult_SeqBAIJ_N,
3032: /* 4*/ MatMultAdd_SeqBAIJ_N,
3033: MatMultTranspose_SeqBAIJ,
3034: MatMultTransposeAdd_SeqBAIJ,
3035: NULL,
3036: NULL,
3037: NULL,
3038: /* 10*/ NULL,
3039: MatLUFactor_SeqBAIJ,
3040: NULL,
3041: NULL,
3042: MatTranspose_SeqBAIJ,
3043: /* 15*/ MatGetInfo_SeqBAIJ,
3044: MatEqual_SeqBAIJ,
3045: MatGetDiagonal_SeqBAIJ,
3046: MatDiagonalScale_SeqBAIJ,
3047: MatNorm_SeqBAIJ,
3048: /* 20*/ NULL,
3049: MatAssemblyEnd_SeqBAIJ,
3050: MatSetOption_SeqBAIJ,
3051: MatZeroEntries_SeqBAIJ,
3052: /* 24*/ MatZeroRows_SeqBAIJ,
3053: NULL,
3054: NULL,
3055: NULL,
3056: NULL,
3057: /* 29*/ MatSetUp_Seq_Hash,
3058: NULL,
3059: NULL,
3060: NULL,
3061: NULL,
3062: /* 34*/ MatDuplicate_SeqBAIJ,
3063: NULL,
3064: NULL,
3065: MatILUFactor_SeqBAIJ,
3066: NULL,
3067: /* 39*/ MatAXPY_SeqBAIJ,
3068: MatCreateSubMatrices_SeqBAIJ,
3069: MatIncreaseOverlap_SeqBAIJ,
3070: MatGetValues_SeqBAIJ,
3071: MatCopy_SeqBAIJ,
3072: /* 44*/ NULL,
3073: MatScale_SeqBAIJ,
3074: MatShift_SeqBAIJ,
3075: NULL,
3076: MatZeroRowsColumns_SeqBAIJ,
3077: /* 49*/ NULL,
3078: MatGetRowIJ_SeqBAIJ,
3079: MatRestoreRowIJ_SeqBAIJ,
3080: MatGetColumnIJ_SeqBAIJ,
3081: MatRestoreColumnIJ_SeqBAIJ,
3082: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3083: NULL,
3084: NULL,
3085: NULL,
3086: MatSetValuesBlocked_SeqBAIJ,
3087: /* 59*/ MatCreateSubMatrix_SeqBAIJ,
3088: MatDestroy_SeqBAIJ,
3089: MatView_SeqBAIJ,
3090: NULL,
3091: NULL,
3092: /* 64*/ NULL,
3093: NULL,
3094: NULL,
3095: NULL,
3096: MatGetRowMaxAbs_SeqBAIJ,
3097: /* 69*/ NULL,
3098: MatConvert_Basic,
3099: NULL,
3100: MatFDColoringApply_BAIJ,
3101: NULL,
3102: /* 74*/ NULL,
3103: NULL,
3104: NULL,
3105: NULL,
3106: MatLoad_SeqBAIJ,
3107: /* 79*/ NULL,
3108: NULL,
3109: NULL,
3110: NULL,
3111: NULL,
3112: /* 84*/ NULL,
3113: NULL,
3114: NULL,
3115: NULL,
3116: NULL,
3117: /* 89*/ NULL,
3118: NULL,
3119: NULL,
3120: NULL,
3121: MatConjugate_SeqBAIJ,
3122: /* 94*/ NULL,
3123: NULL,
3124: MatRealPart_SeqBAIJ,
3125: MatImaginaryPart_SeqBAIJ,
3126: NULL,
3127: /* 99*/ NULL,
3128: NULL,
3129: NULL,
3130: NULL,
3131: NULL,
3132: /*104*/ MatMissingDiagonal_SeqBAIJ,
3133: NULL,
3134: NULL,
3135: NULL,
3136: NULL,
3137: /*109*/ NULL,
3138: NULL,
3139: NULL,
3140: MatMultHermitianTranspose_SeqBAIJ,
3141: MatMultHermitianTransposeAdd_SeqBAIJ,
3142: /*114*/ NULL,
3143: NULL,
3144: MatGetColumnReductions_SeqBAIJ,
3145: MatInvertBlockDiagonal_SeqBAIJ,
3146: NULL,
3147: /*119*/ NULL,
3148: NULL,
3149: NULL,
3150: NULL,
3151: NULL,
3152: /*124*/ NULL,
3153: NULL,
3154: NULL,
3155: MatSetBlockSizes_Default,
3156: NULL,
3157: /*129*/ MatFDColoringSetUp_SeqXAIJ,
3158: NULL,
3159: MatCreateMPIMatConcatenateSeqMat_SeqBAIJ,
3160: MatDestroySubMatrices_SeqBAIJ,
3161: NULL,
3162: /*134*/ NULL,
3163: NULL,
3164: NULL,
3165: MatEliminateZeros_SeqBAIJ,
3166: MatGetRowSumAbs_SeqBAIJ,
3167: /*139*/ NULL,
3168: NULL,
3169: NULL,
3170: MatCopyHashToXAIJ_Seq_Hash,
3171: NULL,
3172: NULL};
3174: static PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat)
3175: {
3176: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3177: PetscInt nz = aij->i[aij->mbs] * aij->bs2;
3179: PetscFunctionBegin;
3180: PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3182: /* allocate space for values if not already there */
3183: if (!aij->saved_values) PetscCall(PetscMalloc1(nz + 1, &aij->saved_values));
3185: /* copy values over */
3186: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3187: PetscFunctionReturn(PETSC_SUCCESS);
3188: }
3190: static PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat)
3191: {
3192: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3193: PetscInt nz = aij->i[aij->mbs] * aij->bs2;
3195: PetscFunctionBegin;
3196: PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3197: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3199: /* copy values over */
3200: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3201: PetscFunctionReturn(PETSC_SUCCESS);
3202: }
3204: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
3205: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *);
3207: PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[])
3208: {
3209: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data;
3210: PetscInt i, mbs, nbs, bs2;
3211: PetscBool flg = PETSC_FALSE, skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3213: PetscFunctionBegin;
3214: if (B->hash_active) {
3215: PetscInt bs;
3216: B->ops[0] = b->cops;
3217: PetscCall(PetscHMapIJVDestroy(&b->ht));
3218: PetscCall(MatGetBlockSize(B, &bs));
3219: if (bs > 1) PetscCall(PetscHSetIJDestroy(&b->bht));
3220: PetscCall(PetscFree(b->dnz));
3221: PetscCall(PetscFree(b->bdnz));
3222: B->hash_active = PETSC_FALSE;
3223: }
3224: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3225: if (nz == MAT_SKIP_ALLOCATION) {
3226: skipallocation = PETSC_TRUE;
3227: nz = 0;
3228: }
3230: PetscCall(MatSetBlockSize(B, bs));
3231: PetscCall(PetscLayoutSetUp(B->rmap));
3232: PetscCall(PetscLayoutSetUp(B->cmap));
3233: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
3235: B->preallocated = PETSC_TRUE;
3237: mbs = B->rmap->n / bs;
3238: nbs = B->cmap->n / bs;
3239: bs2 = bs * bs;
3241: PetscCheck(mbs * bs == B->rmap->n && nbs * bs == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number rows %" PetscInt_FMT ", cols %" PetscInt_FMT " must be divisible by blocksize %" PetscInt_FMT, B->rmap->N, B->cmap->n, bs);
3243: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3244: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3245: if (nnz) {
3246: for (i = 0; i < mbs; i++) {
3247: 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]);
3248: PetscCheck(nnz[i] <= nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than block row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], nbs);
3249: }
3250: }
3252: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Optimize options for SEQBAIJ matrix 2 ", "Mat");
3253: PetscCall(PetscOptionsBool("-mat_no_unroll", "Do not optimize for block size (slow)", NULL, flg, &flg, NULL));
3254: PetscOptionsEnd();
3256: if (!flg) {
3257: switch (bs) {
3258: case 1:
3259: B->ops->mult = MatMult_SeqBAIJ_1;
3260: B->ops->multadd = MatMultAdd_SeqBAIJ_1;
3261: break;
3262: case 2:
3263: B->ops->mult = MatMult_SeqBAIJ_2;
3264: B->ops->multadd = MatMultAdd_SeqBAIJ_2;
3265: break;
3266: case 3:
3267: B->ops->mult = MatMult_SeqBAIJ_3;
3268: B->ops->multadd = MatMultAdd_SeqBAIJ_3;
3269: break;
3270: case 4:
3271: B->ops->mult = MatMult_SeqBAIJ_4;
3272: B->ops->multadd = MatMultAdd_SeqBAIJ_4;
3273: break;
3274: case 5:
3275: B->ops->mult = MatMult_SeqBAIJ_5;
3276: B->ops->multadd = MatMultAdd_SeqBAIJ_5;
3277: break;
3278: case 6:
3279: B->ops->mult = MatMult_SeqBAIJ_6;
3280: B->ops->multadd = MatMultAdd_SeqBAIJ_6;
3281: break;
3282: case 7:
3283: B->ops->mult = MatMult_SeqBAIJ_7;
3284: B->ops->multadd = MatMultAdd_SeqBAIJ_7;
3285: break;
3286: case 9: {
3287: PetscInt version = 1;
3288: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3289: switch (version) {
3290: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
3291: case 1:
3292: B->ops->mult = MatMult_SeqBAIJ_9_AVX2;
3293: B->ops->multadd = MatMultAdd_SeqBAIJ_9_AVX2;
3294: PetscCall(PetscInfo(B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3295: break;
3296: #endif
3297: default:
3298: B->ops->mult = MatMult_SeqBAIJ_N;
3299: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3300: PetscCall(PetscInfo(B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3301: break;
3302: }
3303: break;
3304: }
3305: case 11:
3306: B->ops->mult = MatMult_SeqBAIJ_11;
3307: B->ops->multadd = MatMultAdd_SeqBAIJ_11;
3308: break;
3309: case 12: {
3310: PetscInt version = 1;
3311: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3312: switch (version) {
3313: case 1:
3314: B->ops->mult = MatMult_SeqBAIJ_12_ver1;
3315: B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1;
3316: PetscCall(PetscInfo(B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3317: break;
3318: case 2:
3319: B->ops->mult = MatMult_SeqBAIJ_12_ver2;
3320: B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver2;
3321: PetscCall(PetscInfo(B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3322: break;
3323: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
3324: case 3:
3325: B->ops->mult = MatMult_SeqBAIJ_12_AVX2;
3326: B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1;
3327: PetscCall(PetscInfo(B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3328: break;
3329: #endif
3330: default:
3331: B->ops->mult = MatMult_SeqBAIJ_N;
3332: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3333: PetscCall(PetscInfo(B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3334: break;
3335: }
3336: break;
3337: }
3338: case 15: {
3339: PetscInt version = 1;
3340: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3341: switch (version) {
3342: case 1:
3343: B->ops->mult = MatMult_SeqBAIJ_15_ver1;
3344: PetscCall(PetscInfo(B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3345: break;
3346: case 2:
3347: B->ops->mult = MatMult_SeqBAIJ_15_ver2;
3348: PetscCall(PetscInfo(B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3349: break;
3350: case 3:
3351: B->ops->mult = MatMult_SeqBAIJ_15_ver3;
3352: PetscCall(PetscInfo(B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3353: break;
3354: case 4:
3355: B->ops->mult = MatMult_SeqBAIJ_15_ver4;
3356: PetscCall(PetscInfo(B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3357: break;
3358: default:
3359: B->ops->mult = MatMult_SeqBAIJ_N;
3360: PetscCall(PetscInfo(B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3361: break;
3362: }
3363: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3364: break;
3365: }
3366: default:
3367: B->ops->mult = MatMult_SeqBAIJ_N;
3368: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3369: PetscCall(PetscInfo(B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3370: break;
3371: }
3372: }
3373: B->ops->sor = MatSOR_SeqBAIJ;
3374: b->mbs = mbs;
3375: b->nbs = nbs;
3376: if (!skipallocation) {
3377: if (!b->imax) {
3378: PetscCall(PetscMalloc2(mbs, &b->imax, mbs, &b->ilen));
3380: b->free_imax_ilen = PETSC_TRUE;
3381: }
3382: /* b->ilen will count nonzeros in each block row so far. */
3383: for (i = 0; i < mbs; i++) b->ilen[i] = 0;
3384: if (!nnz) {
3385: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3386: else if (nz < 0) nz = 1;
3387: nz = PetscMin(nz, nbs);
3388: for (i = 0; i < mbs; i++) b->imax[i] = nz;
3389: PetscCall(PetscIntMultError(nz, mbs, &nz));
3390: } else {
3391: PetscInt64 nz64 = 0;
3392: for (i = 0; i < mbs; i++) {
3393: b->imax[i] = nnz[i];
3394: nz64 += nnz[i];
3395: }
3396: PetscCall(PetscIntCast(nz64, &nz));
3397: }
3399: /* allocate the matrix space */
3400: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
3401: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
3402: PetscCall(PetscShmgetAllocateArray(B->rmap->N + 1, sizeof(PetscInt), (void **)&b->i));
3403: if (B->structure_only) {
3404: b->free_a = PETSC_FALSE;
3405: } else {
3406: PetscInt nzbs2 = 0;
3407: PetscCall(PetscIntMultError(nz, bs2, &nzbs2));
3408: PetscCall(PetscShmgetAllocateArray(nzbs2, sizeof(PetscScalar), (void **)&b->a));
3409: b->free_a = PETSC_TRUE;
3410: PetscCall(PetscArrayzero(b->a, nzbs2));
3411: }
3412: b->free_ij = PETSC_TRUE;
3413: PetscCall(PetscArrayzero(b->j, nz));
3415: b->i[0] = 0;
3416: for (i = 1; i < mbs + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
3417: } else {
3418: b->free_a = PETSC_FALSE;
3419: b->free_ij = PETSC_FALSE;
3420: }
3422: b->bs2 = bs2;
3423: b->mbs = mbs;
3424: b->nz = 0;
3425: b->maxnz = nz;
3426: B->info.nz_unneeded = (PetscReal)b->maxnz * bs2;
3427: B->was_assembled = PETSC_FALSE;
3428: B->assembled = PETSC_FALSE;
3429: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
3430: PetscFunctionReturn(PETSC_SUCCESS);
3431: }
3433: static PetscErrorCode MatSeqBAIJSetPreallocationCSR_SeqBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
3434: {
3435: PetscInt i, m, nz, nz_max = 0, *nnz;
3436: PetscScalar *values = NULL;
3437: PetscBool roworiented = ((Mat_SeqBAIJ *)B->data)->roworiented;
3439: PetscFunctionBegin;
3440: PetscCheck(bs >= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Invalid block size specified, must be positive but it is %" PetscInt_FMT, bs);
3441: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
3442: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
3443: PetscCall(PetscLayoutSetUp(B->rmap));
3444: PetscCall(PetscLayoutSetUp(B->cmap));
3445: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
3446: m = B->rmap->n / bs;
3448: PetscCheck(ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
3449: PetscCall(PetscMalloc1(m + 1, &nnz));
3450: for (i = 0; i < m; i++) {
3451: nz = ii[i + 1] - ii[i];
3452: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
3453: nz_max = PetscMax(nz_max, nz);
3454: nnz[i] = nz;
3455: }
3456: PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz));
3457: PetscCall(PetscFree(nnz));
3459: values = (PetscScalar *)V;
3460: if (!values) PetscCall(PetscCalloc1(bs * bs * (nz_max + 1), &values));
3461: for (i = 0; i < m; i++) {
3462: PetscInt ncols = ii[i + 1] - ii[i];
3463: const PetscInt *icols = jj + ii[i];
3464: if (bs == 1 || !roworiented) {
3465: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
3466: PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, ncols, icols, svals, INSERT_VALUES));
3467: } else {
3468: PetscInt j;
3469: for (j = 0; j < ncols; j++) {
3470: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
3471: PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, 1, &icols[j], svals, INSERT_VALUES));
3472: }
3473: }
3474: }
3475: if (!V) PetscCall(PetscFree(values));
3476: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3477: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3478: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3479: PetscFunctionReturn(PETSC_SUCCESS);
3480: }
3482: /*@C
3483: MatSeqBAIJGetArray - gives read/write access to the array where the data for a `MATSEQBAIJ` matrix is stored
3485: Not Collective
3487: Input Parameter:
3488: . A - a `MATSEQBAIJ` matrix
3490: Output Parameter:
3491: . array - pointer to the data
3493: Level: intermediate
3495: .seealso: [](ch_matrices), `Mat`, `MATSEQBAIJ`, `MatSeqBAIJRestoreArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`
3496: @*/
3497: PetscErrorCode MatSeqBAIJGetArray(Mat A, PetscScalar *array[])
3498: {
3499: PetscFunctionBegin;
3500: PetscUseMethod(A, "MatSeqBAIJGetArray_C", (Mat, PetscScalar **), (A, array));
3501: PetscFunctionReturn(PETSC_SUCCESS);
3502: }
3504: /*@C
3505: MatSeqBAIJRestoreArray - returns access to the array where the data for a `MATSEQBAIJ` matrix is stored obtained by `MatSeqBAIJGetArray()`
3507: Not Collective
3509: Input Parameters:
3510: + A - a `MATSEQBAIJ` matrix
3511: - array - pointer to the data
3513: Level: intermediate
3515: .seealso: [](ch_matrices), `Mat`, `MatSeqBAIJGetArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`
3516: @*/
3517: PetscErrorCode MatSeqBAIJRestoreArray(Mat A, PetscScalar *array[])
3518: {
3519: PetscFunctionBegin;
3520: PetscUseMethod(A, "MatSeqBAIJRestoreArray_C", (Mat, PetscScalar **), (A, array));
3521: PetscFunctionReturn(PETSC_SUCCESS);
3522: }
3524: /*MC
3525: MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on
3526: block sparse compressed row format.
3528: Options Database Keys:
3529: + -mat_type seqbaij - sets the matrix type to `MATSEQBAIJ` during a call to `MatSetFromOptions()`
3530: - -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
3532: Level: beginner
3534: Notes:
3535: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
3536: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
3538: Run with `-info` to see what version of the matrix-vector product is being used
3540: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`
3541: M*/
3543: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBSTRM(Mat, MatType, MatReuse, Mat *);
3545: PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJ(Mat B)
3546: {
3547: PetscMPIInt size;
3548: Mat_SeqBAIJ *b;
3550: PetscFunctionBegin;
3551: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
3552: PetscCheck(size == 1, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Comm must be of size 1");
3554: PetscCall(PetscNew(&b));
3555: B->data = (void *)b;
3556: B->ops[0] = MatOps_Values;
3558: b->row = NULL;
3559: b->col = NULL;
3560: b->icol = NULL;
3561: b->reallocs = 0;
3562: b->saved_values = NULL;
3564: b->roworiented = PETSC_TRUE;
3565: b->nonew = 0;
3566: b->diag = NULL;
3567: B->spptr = NULL;
3568: B->info.nz_unneeded = (PetscReal)b->maxnz * b->bs2;
3569: b->keepnonzeropattern = PETSC_FALSE;
3571: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJGetArray_C", MatSeqBAIJGetArray_SeqBAIJ));
3572: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJRestoreArray_C", MatSeqBAIJRestoreArray_SeqBAIJ));
3573: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqBAIJ));
3574: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqBAIJ));
3575: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetColumnIndices_C", MatSeqBAIJSetColumnIndices_SeqBAIJ));
3576: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqaij_C", MatConvert_SeqBAIJ_SeqAIJ));
3577: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqsbaij_C", MatConvert_SeqBAIJ_SeqSBAIJ));
3578: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocation_C", MatSeqBAIJSetPreallocation_SeqBAIJ));
3579: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocationCSR_C", MatSeqBAIJSetPreallocationCSR_SeqBAIJ));
3580: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqBAIJ));
3581: #if defined(PETSC_HAVE_HYPRE)
3582: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_hypre_C", MatConvert_AIJ_HYPRE));
3583: #endif
3584: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_is_C", MatConvert_XAIJ_IS));
3585: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJ));
3586: PetscFunctionReturn(PETSC_SUCCESS);
3587: }
3589: PETSC_INTERN PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
3590: {
3591: Mat_SeqBAIJ *c = (Mat_SeqBAIJ *)C->data, *a = (Mat_SeqBAIJ *)A->data;
3592: PetscInt i, mbs = a->mbs, nz = a->nz, bs2 = a->bs2;
3594: PetscFunctionBegin;
3595: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
3596: PetscCheck(a->i[mbs] == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupt matrix");
3598: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3599: c->imax = a->imax;
3600: c->ilen = a->ilen;
3601: c->free_imax_ilen = PETSC_FALSE;
3602: } else {
3603: PetscCall(PetscMalloc2(mbs, &c->imax, mbs, &c->ilen));
3604: for (i = 0; i < mbs; i++) {
3605: c->imax[i] = a->imax[i];
3606: c->ilen[i] = a->ilen[i];
3607: }
3608: c->free_imax_ilen = PETSC_TRUE;
3609: }
3611: /* allocate the matrix space */
3612: if (mallocmatspace) {
3613: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3614: PetscCall(PetscShmgetAllocateArray(bs2 * nz, sizeof(PetscScalar), (void **)&c->a));
3615: PetscCall(PetscArrayzero(c->a, bs2 * nz));
3616: c->free_a = PETSC_TRUE;
3617: c->i = a->i;
3618: c->j = a->j;
3619: c->free_ij = PETSC_FALSE;
3620: c->parent = A;
3621: C->preallocated = PETSC_TRUE;
3622: C->assembled = PETSC_TRUE;
3624: PetscCall(PetscObjectReference((PetscObject)A));
3625: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3626: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3627: } else {
3628: PetscCall(PetscShmgetAllocateArray(bs2 * nz, sizeof(PetscScalar), (void **)&c->a));
3629: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&c->j));
3630: PetscCall(PetscShmgetAllocateArray(mbs + 1, sizeof(PetscInt), (void **)&c->i));
3631: c->free_a = PETSC_TRUE;
3632: c->free_ij = PETSC_TRUE;
3634: PetscCall(PetscArraycpy(c->i, a->i, mbs + 1));
3635: if (mbs > 0) {
3636: PetscCall(PetscArraycpy(c->j, a->j, nz));
3637: if (cpvalues == MAT_COPY_VALUES) {
3638: PetscCall(PetscArraycpy(c->a, a->a, bs2 * nz));
3639: } else {
3640: PetscCall(PetscArrayzero(c->a, bs2 * nz));
3641: }
3642: }
3643: C->preallocated = PETSC_TRUE;
3644: C->assembled = PETSC_TRUE;
3645: }
3646: }
3648: c->roworiented = a->roworiented;
3649: c->nonew = a->nonew;
3651: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
3652: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
3654: c->bs2 = a->bs2;
3655: c->mbs = a->mbs;
3656: c->nbs = a->nbs;
3658: if (a->diag) {
3659: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3660: c->diag = a->diag;
3661: c->free_diag = PETSC_FALSE;
3662: } else {
3663: PetscCall(PetscMalloc1(mbs + 1, &c->diag));
3664: for (i = 0; i < mbs; i++) c->diag[i] = a->diag[i];
3665: c->free_diag = PETSC_TRUE;
3666: }
3667: } else c->diag = NULL;
3669: c->nz = a->nz;
3670: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
3671: c->solve_work = NULL;
3672: c->mult_work = NULL;
3673: c->sor_workt = NULL;
3674: c->sor_work = NULL;
3676: c->compressedrow.use = a->compressedrow.use;
3677: c->compressedrow.nrows = a->compressedrow.nrows;
3678: if (a->compressedrow.use) {
3679: i = a->compressedrow.nrows;
3680: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i + 1, &c->compressedrow.rindex));
3681: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
3682: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
3683: } else {
3684: c->compressedrow.use = PETSC_FALSE;
3685: c->compressedrow.i = NULL;
3686: c->compressedrow.rindex = NULL;
3687: }
3688: c->nonzerorowcnt = a->nonzerorowcnt;
3689: C->nonzerostate = A->nonzerostate;
3691: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
3692: PetscFunctionReturn(PETSC_SUCCESS);
3693: }
3695: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
3696: {
3697: PetscFunctionBegin;
3698: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
3699: PetscCall(MatSetSizes(*B, A->rmap->N, A->cmap->n, A->rmap->N, A->cmap->n));
3700: PetscCall(MatSetType(*B, MATSEQBAIJ));
3701: PetscCall(MatDuplicateNoCreate_SeqBAIJ(*B, A, cpvalues, PETSC_TRUE));
3702: PetscFunctionReturn(PETSC_SUCCESS);
3703: }
3705: /* Used for both SeqBAIJ and SeqSBAIJ matrices */
3706: PetscErrorCode MatLoad_SeqBAIJ_Binary(Mat mat, PetscViewer viewer)
3707: {
3708: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3709: PetscInt *rowidxs, *colidxs;
3710: PetscScalar *matvals;
3712: PetscFunctionBegin;
3713: PetscCall(PetscViewerSetUp(viewer));
3715: /* read matrix header */
3716: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3717: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3718: M = header[1];
3719: N = header[2];
3720: nz = header[3];
3721: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3722: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3723: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqBAIJ");
3725: /* set block sizes from the viewer's .info file */
3726: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3727: /* set local and global sizes if not set already */
3728: if (mat->rmap->n < 0) mat->rmap->n = M;
3729: if (mat->cmap->n < 0) mat->cmap->n = N;
3730: if (mat->rmap->N < 0) mat->rmap->N = M;
3731: if (mat->cmap->N < 0) mat->cmap->N = N;
3732: PetscCall(PetscLayoutSetUp(mat->rmap));
3733: PetscCall(PetscLayoutSetUp(mat->cmap));
3735: /* check if the matrix sizes are correct */
3736: PetscCall(MatGetSize(mat, &rows, &cols));
3737: 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);
3738: PetscCall(MatGetBlockSize(mat, &bs));
3739: PetscCall(MatGetLocalSize(mat, &m, &n));
3740: mbs = m / bs;
3741: nbs = n / bs;
3743: /* read in row lengths, column indices and nonzero values */
3744: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3745: PetscCall(PetscViewerBinaryRead(viewer, rowidxs + 1, m, NULL, PETSC_INT));
3746: rowidxs[0] = 0;
3747: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3748: sum = rowidxs[m];
3749: 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);
3751: /* read in column indices and nonzero values */
3752: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, nz, &matvals));
3753: PetscCall(PetscViewerBinaryRead(viewer, colidxs, rowidxs[m], NULL, PETSC_INT));
3754: PetscCall(PetscViewerBinaryRead(viewer, matvals, rowidxs[m], NULL, PETSC_SCALAR));
3756: { /* preallocate matrix storage */
3757: PetscBT bt; /* helper bit set to count nonzeros */
3758: PetscInt *nnz;
3759: PetscBool sbaij;
3761: PetscCall(PetscBTCreate(nbs, &bt));
3762: PetscCall(PetscCalloc1(mbs, &nnz));
3763: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATSEQSBAIJ, &sbaij));
3764: for (i = 0; i < mbs; i++) {
3765: PetscCall(PetscBTMemzero(nbs, bt));
3766: for (k = 0; k < bs; k++) {
3767: PetscInt row = bs * i + k;
3768: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3769: PetscInt col = colidxs[j];
3770: if (!sbaij || col >= row)
3771: if (!PetscBTLookupSet(bt, col / bs)) nnz[i]++;
3772: }
3773: }
3774: }
3775: PetscCall(PetscBTDestroy(&bt));
3776: PetscCall(MatSeqBAIJSetPreallocation(mat, bs, 0, nnz));
3777: PetscCall(MatSeqSBAIJSetPreallocation(mat, bs, 0, nnz));
3778: PetscCall(PetscFree(nnz));
3779: }
3781: /* store matrix values */
3782: for (i = 0; i < m; i++) {
3783: PetscInt row = i, s = rowidxs[i], e = rowidxs[i + 1];
3784: PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES);
3785: }
3787: PetscCall(PetscFree(rowidxs));
3788: PetscCall(PetscFree2(colidxs, matvals));
3789: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3790: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3791: PetscFunctionReturn(PETSC_SUCCESS);
3792: }
3794: PetscErrorCode MatLoad_SeqBAIJ(Mat mat, PetscViewer viewer)
3795: {
3796: PetscBool isbinary;
3798: PetscFunctionBegin;
3799: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3800: PetscCheck(isbinary, PetscObjectComm((PetscObject)viewer), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)mat)->type_name);
3801: PetscCall(MatLoad_SeqBAIJ_Binary(mat, viewer));
3802: PetscFunctionReturn(PETSC_SUCCESS);
3803: }
3805: /*@
3806: MatCreateSeqBAIJ - Creates a sparse matrix in `MATSEQAIJ` (block
3807: compressed row) format. For good matrix assembly performance the
3808: user should preallocate the matrix storage by setting the parameter `nz`
3809: (or the array `nnz`).
3811: Collective
3813: Input Parameters:
3814: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3815: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3816: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3817: . m - number of rows
3818: . n - number of columns
3819: . nz - number of nonzero blocks per block row (same for all rows)
3820: - nnz - array containing the number of nonzero blocks in the various block rows
3821: (possibly different for each block row) or `NULL`
3823: Output Parameter:
3824: . A - the matrix
3826: Options Database Keys:
3827: + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
3828: - -mat_block_size - size of the blocks to use
3830: Level: intermediate
3832: Notes:
3833: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3834: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3835: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
3837: The number of rows and columns must be divisible by blocksize.
3839: If the `nnz` parameter is given then the `nz` parameter is ignored
3841: A nonzero block is any block that as 1 or more nonzeros in it
3843: The `MATSEQBAIJ` format is fully compatible with standard Fortran
3844: storage. That is, the stored row and column indices can begin at
3845: either one (as in Fortran) or zero.
3847: Specify the preallocated storage with either `nz` or `nnz` (not both).
3848: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3849: allocation. See [Sparse Matrices](sec_matsparse) for details.
3850: matrices.
3852: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`
3853: @*/
3854: PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3855: {
3856: PetscFunctionBegin;
3857: PetscCall(MatCreate(comm, A));
3858: PetscCall(MatSetSizes(*A, m, n, m, n));
3859: PetscCall(MatSetType(*A, MATSEQBAIJ));
3860: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, nz, (PetscInt *)nnz));
3861: PetscFunctionReturn(PETSC_SUCCESS);
3862: }
3864: /*@
3865: MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
3866: per row in the matrix. For good matrix assembly performance the
3867: user should preallocate the matrix storage by setting the parameter `nz`
3868: (or the array `nnz`).
3870: Collective
3872: Input Parameters:
3873: + B - the matrix
3874: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3875: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3876: . nz - number of block nonzeros per block row (same for all rows)
3877: - nnz - array containing the number of block nonzeros in the various block rows
3878: (possibly different for each block row) or `NULL`
3880: Options Database Keys:
3881: + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
3882: - -mat_block_size - size of the blocks to use
3884: Level: intermediate
3886: Notes:
3887: If the `nnz` parameter is given then the `nz` parameter is ignored
3889: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3890: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3891: You can also run with the option `-info` and look for messages with the string
3892: malloc in them to see if additional memory allocation was needed.
3894: The `MATSEQBAIJ` format is fully compatible with standard Fortran
3895: storage. That is, the stored row and column indices can begin at
3896: either one (as in Fortran) or zero.
3898: Specify the preallocated storage with either `nz` or `nnz` (not both).
3899: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3900: allocation. See [Sparse Matrices](sec_matsparse) for details.
3902: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatGetInfo()`
3903: @*/
3904: PetscErrorCode MatSeqBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[])
3905: {
3906: PetscFunctionBegin;
3910: PetscTryMethod(B, "MatSeqBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[]), (B, bs, nz, nnz));
3911: PetscFunctionReturn(PETSC_SUCCESS);
3912: }
3914: /*@C
3915: MatSeqBAIJSetPreallocationCSR - Creates a sparse sequential matrix in `MATSEQBAIJ` format using the given nonzero structure and (optional) numerical values
3917: Collective
3919: Input Parameters:
3920: + B - the matrix
3921: . bs - the blocksize
3922: . i - the indices into `j` for the start of each local row (indices start with zero)
3923: . j - the column indices for each local row (indices start with zero) these must be sorted for each row
3924: - v - optional values in the matrix, use `NULL` if not provided
3926: Level: advanced
3928: Notes:
3929: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqBAIJWithArrays()`
3931: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
3932: may want to use the default `MAT_ROW_ORIENTED` of `PETSC_TRUE` and use an array v[nnz][bs][bs] where the second index is
3933: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
3934: `MAT_ROW_ORIENTED` of `PETSC_FALSE` and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
3935: block column and the second index is over columns within a block.
3937: Though this routine has Preallocation() in the name it also sets the exact nonzero locations of the matrix entries and usually the numerical values as well
3939: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatSeqBAIJSetPreallocation()`, `MATSEQBAIJ`
3940: @*/
3941: PetscErrorCode MatSeqBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3942: {
3943: PetscFunctionBegin;
3947: PetscTryMethod(B, "MatSeqBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
3948: PetscFunctionReturn(PETSC_SUCCESS);
3949: }
3951: /*@
3952: MatCreateSeqBAIJWithArrays - Creates a `MATSEQBAIJ` matrix using matrix elements provided by the user.
3954: Collective
3956: Input Parameters:
3957: + comm - must be an MPI communicator of size 1
3958: . bs - size of block
3959: . m - number of rows
3960: . n - number of columns
3961: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row block row of the matrix
3962: . j - column indices
3963: - a - matrix values
3965: Output Parameter:
3966: . mat - the matrix
3968: Level: advanced
3970: Notes:
3971: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
3972: once the matrix is destroyed
3974: You cannot set new nonzero locations into this matrix, that will generate an error.
3976: The `i` and `j` indices are 0 based
3978: When block size is greater than 1 the matrix values must be stored using the `MATSEQBAIJ` storage format
3980: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3981: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3982: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3983: with column-major ordering within blocks.
3985: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateBAIJ()`, `MatCreateSeqBAIJ()`
3986: @*/
3987: PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
3988: {
3989: Mat_SeqBAIJ *baij;
3991: PetscFunctionBegin;
3992: PetscCheck(bs == 1, PETSC_COMM_SELF, PETSC_ERR_SUP, "block size %" PetscInt_FMT " > 1 is not supported yet", bs);
3993: if (m > 0) PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3995: PetscCall(MatCreate(comm, mat));
3996: PetscCall(MatSetSizes(*mat, m, n, m, n));
3997: PetscCall(MatSetType(*mat, MATSEQBAIJ));
3998: PetscCall(MatSeqBAIJSetPreallocation(*mat, bs, MAT_SKIP_ALLOCATION, NULL));
3999: baij = (Mat_SeqBAIJ *)(*mat)->data;
4000: PetscCall(PetscMalloc2(m, &baij->imax, m, &baij->ilen));
4002: baij->i = i;
4003: baij->j = j;
4004: baij->a = a;
4006: baij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4007: baij->free_a = PETSC_FALSE;
4008: baij->free_ij = PETSC_FALSE;
4009: baij->free_imax_ilen = PETSC_TRUE;
4011: for (PetscInt ii = 0; ii < m; ii++) {
4012: const PetscInt row_len = i[ii + 1] - i[ii];
4014: baij->ilen[ii] = baij->imax[ii] = row_len;
4015: PetscCheck(row_len >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, row_len);
4016: }
4017: if (PetscDefined(USE_DEBUG)) {
4018: for (PetscInt ii = 0; ii < baij->i[m]; ii++) {
4019: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
4020: PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
4021: }
4022: }
4024: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
4025: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
4026: PetscFunctionReturn(PETSC_SUCCESS);
4027: }
4029: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4030: {
4031: PetscFunctionBegin;
4032: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(comm, inmat, n, scall, outmat));
4033: PetscFunctionReturn(PETSC_SUCCESS);
4034: }